{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (0.13.2)\n",
      "Requirement already satisfied: packaging>=21.3 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from statsmodels) (21.3)\n",
      "Requirement already satisfied: patsy>=0.5.2 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from statsmodels) (0.5.2)\n",
      "Requirement already satisfied: numpy>=1.17 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from statsmodels) (1.19.2)\n",
      "Requirement already satisfied: scipy>=1.3 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from statsmodels) (1.6.2)\n",
      "Requirement already satisfied: pandas>=0.25 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from statsmodels) (1.3.4)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from packaging>=21.3->statsmodels) (3.0.4)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from pandas>=0.25->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from pandas>=0.25->statsmodels) (2021.3)\n",
      "Requirement already satisfied: six in /Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "LspltVTXeWkl"
   },
   "outputs": [],
   "source": [
    "#importing important libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as dates\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "rqEXNnSJe7ME",
    "outputId": "c7ee72bb-6b49-4f32-e63a-4716278a58dc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  #Passengers\n",
       "0  1949-01          112\n",
       "1  1949-02          118\n",
       "2  1949-03          132\n",
       "3  1949-04          129\n",
       "4  1949-05          121"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('AirPassengers.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "IG1rkGZd91Sc",
    "outputId": "eaa042b2-02b1-4762-e4e1-88ae9b6c57f2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-02-01</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-03-01</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-04-01</th>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-05-01</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            #Passengers\n",
       "Month                  \n",
       "1949-01-01          112\n",
       "1949-02-01          118\n",
       "1949-03-01          132\n",
       "1949-04-01          129\n",
       "1949-05-01          121"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert Month object into datetime\n",
    "df['Month'] = pd.to_datetime(df.Month)\n",
    "df = df.set_index(df.Month)\n",
    "df.drop('Month', axis = 1, inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MC1-jwR7e7JQ",
    "outputId": "58f83916-d7f9-4433-975a-a631b5224f82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01    112\n",
       "1949-02-01    118\n",
       "1949-03-01    132\n",
       "1949-04-01    129\n",
       "1949-05-01    121\n",
       "Name: #Passengers, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = df['#Passengers']\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "4gETXVHMYK-P",
    "outputId": "3cc50e9b-f835-496a-8cde-3223bb161602"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fluctuation of customer traffic within october\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (12,4)\n",
    "plt.plot(ts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YBGp-F0CbNcv",
    "outputId": "cd5649a1-efa8-49d8-edcd-02ebc7bf0ed0"
   },
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "#Ho: It is non stationary\n",
    "#H1: It is stationary\n",
    "# A function defined for Dickey Fuller Test\n",
    "def adfuller_test(births):\n",
    "    result=adfuller(births)\n",
    "    labels = ['ADF Test Statistic','p-value','#Lags Used','Number of Observations Used']\n",
    "    for value,label in zip(result,labels):\n",
    "        print(label+' : '+str(value) )\n",
    "    if result[1] <= 0.1:\n",
    "        print(\"strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KMSvXb8hcUnB",
    "outputId": "e890f8df-400b-4766-944b-6423d4ebb5c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : 0.8153688792060544\n",
      "p-value : 0.9918802434376411\n",
      "#Lags Used : 13\n",
      "Number of Observations Used : 130\n",
      "weak evidence against null hypothesis, time series has a unit root, indicating it is non-stationary \n"
     ]
    }
   ],
   "source": [
    "#dickey fuller test for ts\n",
    "adfuller_test(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G60Mw8Gx7FRK"
   },
   "source": [
    "***Since there is a significant positive trend,we can apply log transformation to penalize higher values more than smaller values.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "aA-E-Tn8ku-k",
    "outputId": "0bca157b-1a7e-49d5-a8c6-3e525161867c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa9f68e09d0>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hxOx1vg+sGbL8AxDBXHqZ3+9lunaH+EGaJCullFKqY8mKpdC3Pwwb2fRg/EAn+QryJJk9O6C0pOkGImcxoWHQp3/QtYGTwwehOL9Nq8gAxhjMmAxkaxbidvs4usbE7UaWL4VRaZheff16r3pmWAoU5XW6VnCaJCullFKdlOzbBYW5mGlzMcY0OW6MwYxMRXI3BXWCIlmrwRiPujuY/oMh2JLk9asAMOO9r0euZ8ZmQGUF+HvFNXsNHC3DuvRy/97nTEkjnfe2d1fH3dMHNElWSimlOilZ8SG4QjBTZrQ8KCUVTh6HXds7LC5vyaY1MHQEJqpn64P7D4ZDB5CqSv8H5iFZt8LZQKR3v7ZfZMQ4CAlBcvxbcmF/+h7ExMHYjmvJZoalAC3XJcu2HKS6qsPi8ZQmyUoppVQnJLW1yOefQOpETFSPFseZFGeL4WCtS5aqCqd93QjPtkKuf3iPfcGxKimHDsCOgjZ1tTiTiegCw8cg2Wt9FFlTcnAf5G7CXPwljNWBLfRiezu9uwuaJsly/Bj2H+5H3nqx4+LxkCbJSimlVGe06Qs4cQxrWtMH9s5konrCgCFBmySzoxDExiQmeza+fwIQPNtTN3S1aEepRT0zdgIc2Iv4qcWdfPYeuFzNPuTpT8YYTFIKUrAVEWkc05plUOfGTJ7eoTF5QpNkpZRSqhOyl38IMb0gpfUVWJOSCkW5QfkjbSnOd74YkuTZCbG9ITwiaOqSZd1KGDLcJw/BmVMlEOKHLhdSU42s/AiTOhkT7UFZi68lpcDRUig71Diuzz+GgUMwA4Z0fEyt0CRZKaWU6mSkuhq2ZWMmXuLRj83NqFRwuyF/i/+D85Js3wa94zHdojwabyzLaSm2Z4d/A/OAFBfAzkKfrCIDTqLdd0Cb6pLlwB7sY+UtH1+zDCpOYKZ34AN7Z2ioSz6j5EL27nI+v6kzAxJTazzalvrkyZM888wz7N69G2MMt912G8OHD284LiIsWbKErKwswsPDuf3220lMTARg48aNLFmyBNu2mTVrFvPmzfPLG1FKKaW8JcePgdR59sBYMNlZAHV1mKQUz8YPS4GQUGeL6jHj/RubF0TEaZ020rN65HpmQAKS9Tki0mxXj44gbjf23/4f9IjxafmCGTsB+fgtpKri3D2j6+OwbeT9/yBvvMCRxGTkZ79t8o2TuGuRt19y+jEnj/FZrF7pPwi6RDrdO06VVsjnHznlHxMvDUxMrfBoJXnJkiWkpqbyxBNP8Nhjj9G/f/9Gx7Oysjhw4ABPPvkk3//+93n++ecBsG2bxYsX84tf/II//OEPrFy5kj179vj+XSillFJeEhHsP/wK++6bsV94Bjnrx8DBTIrynC88rOM1YeGQlILkbvRfUG1RdgjKj3j8Phr0HwwnjjvnBoh88Crs2YH1zVub7HTYHmZshrPq78F24nLsKPYff4O89ncYPAx3YS6ybGnTccs+gNISrK9+K2DfVBjLBUNHIIW5Tkx1dcjqz2D0+HM+eBpIrSbJFRUV5ObmMnOmsxQeEhJC166NfzOsW7eOSy65BGMMw4cP5+TJkxw5coTCwkL69u1Lnz59CAkJYerUqaxd67+nNpVSSimP7SiE3cUweBiy/APsX/4A+4U/d4pkWQpzoe8Aj0sUAMyoNNi7Ezla6sfIvCPbnXpkM2R4KyMbM/1Pdbjwc12yHC1ttqxD9u9G3v4XJmMaJvXcG6B4behI6NK11S4XkpeN/eCPoGAL5qYFWPc+RujodOS1vyHHjp4eV1XprCInjznnlt8dwQxLcX4PnjwBuRuhvAxrSnCWWoAHSXJJSQlRUVE8/fTT/PznP+eZZ56hqqpx4X9ZWRlxcXENv46NjaWsrIyysjJiY2ObvK6UUkoFmqz8EELDsO68H2vhs5ips5HlH2L/5k7k5PFAh9ciEYGiPMzQEV6dZ0amOud7sELZYbZvg9AwGJDg3Xn1HS78XJds/+PP2L+5E/vvTyEVJ5x72jb23/4EYRGYb3zP5/c0ISGY0enIpjVIXfMbwMjendi//zV06Yr1i8exLvkSxhiivv9TqK5GXvnr6bGZb8Lxcqyv3hSwVeR69XXJFOUiqz6GyG4wtvUNZAKl1Zrkuro6iouLmT9/PklJSSxZsoTXX3+d66+/vmHM2e08wGn30dLrzcnMzCQzMxOARYsWNUq6lfdCQkL0MwxCOi/BSeclePlrbqS6mkNrVxAxdQbRg06tSib/mpo5V3HkVwvotrOALpd0bJssT7n37qT05HG6p06gixefjcTEcKh7NOE784m++rp2xeCreSnbsx2GJhPT18tNOOLiONx/EK6CzfT8pu8TVXBym0Pbt2F69cFe8SEmZx3dbvlv7KNHOF6YS9QP76NLoocdObxUNeNyytcuJ+rgbsKb2fTj+LsvU2EZei16FuuMThUhIX2JnHcDFf/5G1FXfo2QgUM4/OHrhE+6hB6T2rZlti9J98mUhIQQXrCZyk1f0GXml4nq144NWPys1SQ5NjaW2NhYkpKc3wiTJ0/m9ddfbzLm8OHDDb8uLS2lZ8+euN1uSktLm7zenNmzZzN79uyGX595PeW9uLg4/QyDkM5LcNJ5CV7+mhv7i8+QihPUZFzc6PrSuz90j+b4yo85mZLu8/v6gr3ucwBO9BnASS8/G0lKoWrTOmrb+Zn6Yl7EXYtdmIeZcUWbrmWPnUjdB69yaGcxpmv3dsXSbHyHDyLHy+Hqb2ANGY79tz9R/th9YCxISePEmAlef/4e33twEoRHUP7RO1jxCY2PiWAvWwojx1FWWwdnxBAXF0fVjKvg0/c58vQjmJHjkMpKai+/Lnj+jhs0lMqlb4BtU50+NeBxxcfHt3is1XKLHj16EBsby759TmPrnJwcBgwY0GhMRkYGy5YtQ0TIz88nMjKSnj17MnToUPbv309JSQlut5tVq1aRkdFx2yAqpZRSzZGVmRDXB4aPbvS6sSzM2Axk8/oWf9QdcEV5zo+p+/RvfexZzPAxUFqCHD7oh8C8tGcHuGthiJcP7Z1i0qeAbSMb1/g2rno7Cpz7JCRhBg/D+sXvMNd8FwYPxbrxNr+WLpiwcMy4iciGz5v+PtxRCKUlmIyLmz83PBzrG9+HfbuQj97CTJmB6T/Ib7F6ywxLAduGfgMhwT8r8b7iUQu4+fPn8+STT+J2u+nduze33347S5c6T0/OnTuXtLQ0NmzYwJ133klYWBi33347AC6Xi/nz57Nw4UJs22bGjBkMHDjQf+9GKaWUaoUcPuhszfuVG5yeu2cxYycgKz+CotwmSXQwkMJcGDqi2dhbY5JHI4Dkb8bE9fF9cF6Q7ducmLztbFFv8DCI6YVkfQ4XzfJhZA7ZUQAhIQ310sblwnzpq/Clr/r8Xs0xGdOc3sbbciAl9XRc65aDKwSTOqnlc8dNhHETYUsW5upvdEC0njNJI5Glr2GmzAx4jXRrPEqSExISWLRoUaPX5s49XatljOGWW25p9tz09HTS04PzR1ZKKaUuPLLqIzAGM6WFxColFVwhyKa1mCBLkuXkCdi/GzOpjX1l4wdBt+6Qvxmm+j6x9EpxPkT3hJi21TYbYzDpU5BP3/O4p7A3ZEchDBiCCQn16XU9NioNwrsg61Y4OyZyqifyuhUwKg3Ttds5T7d+cDccLcXE9u6AYL0wajxm3o2YSy8LdCSt0h33lFJKXTDEtp1V4pGpmNhezY4xEZGQPLrVFlwBUb/66mVni3rGsmD4aGTbZl9G1SayfRsMSW7XaqJJmwLuWiRnvQ8jc36fsLMQE8BygIaSi6zPEbfbebE4H8oOYya0/hCeCQ31yVbZvmZCQ7GuvM6nvaX9RZNkpZRSF468bCg7hJk2+5zDzNgJcGAPUrKvgwLzjBTlgmW1q5bTDB8Dhw8ipSU+jMw7cuIYlOzHJHrXH7mJYSOgezRs+Nw3gdU7uBeqKgNeM2smXORsmrItBwBZuxxCQjHjWi61UL6jSbJSSqkLhqzMhMhu56znhFNJMiDZ6zoiLI9JUZ5TAhDRpc3XMMlOCUlAV5OLT20i0tZ65FOM5cKkTUFy1iG1Nb6IDAApPv3QXkCNSoeILsj6lc5PQdavdHao6+Lb0hLVPE2SlVJKXRCktgbJWo2ZeAkmNOycY02vvtBvYFCVXEhdHRTnt7nUokFDXXKObwJrA9me77RSGzys3dcy6VOgugq2ZPkgslN2FkJ4BPTzvoOIL5nQsIYuF2zLgaNlmIyLAhrThUSTZKWUUheG4gKorXG2Z/aAGTsB8rcglRV+DsxDe3c4yeCwke26TDDUJcv2bdB/ULtWxBskj4bIrk4i6SOyowAGJWIsl8+u2VYmYxqcPI79r79AmJM0q46hSbJSSimvyP49SFVloMPwmhRscb5ISvFovBk7AercsNWHK5TtIEV5AJih7UuSIbB1yWLbsCMfM6Sd9cinmJBQzNiJzjbO9Q+4tYO43bC7OPClFvVGpUGXSNi3C8Zk+OYbC+URTZKVUkp5TGprsBfehf3E/UhtbaDD8YoUbIH+gz3fnW3oCIjshmwKkpKLwjzoEdvmlmlnMsmjAP/UJcuGz7E/fQ8RaX5A7iaoOAk+SpIBzPgpUHHC6xISsZvZMGbfTqitCfhDe/XqSy4ArIzAby19IdEkWSmllOcKc6G6EorykBeebjkRCjJSVweFeZjhozw+x7hcmDHjnd33mkumOpgU5WKGjvDNBgzxg6Gr7+uSRQT75cXIC39GXn+hye8P2VOM/ewj0G8gZrwPa2tT0iAs3KuSC6mqxL7/Duy/P9UoTtkRJA/tncHMvhpSJ8GYCYEO5YKiSbJSSimPSe4mcLkws7+CrPwI+eitQIfkmd3bneQ+yfMkGYCxE+B4uVPP7Ef2Oy9j//WPSNmhZo/L3l1QWuK0PPMBpy55lO9Xkkv2O3H2jkfefRl543SiLKUl2E/8BsK7YP3oAZ/2yTVh4ZCSimze4PE3bvL2S3BgL7LsA2T50tMHdhQ630AEUY9hM3gYrgW/xISHBzqUC4omyUoppTwmedkwZDjm2u9C6mTk5f9FfNlVwE8k36lHNh7WI9czo9LBWMiWDf4IC3BWXyXzTWTlR9i/uh377X8hNdXOsfIj2C8+h/3Qj52HtkZn+Oy+Jtn3dclyqn7buvPXmGlzkHdeRt58ETlxDPuJ+6GmGuvHD7S4kUt7mJRUJ0E/tL/1OPfvQTLfxEyZCaPSkBefQ3YWOcd2FMDgYUG/ZbLyP02SlVJKeUQqTsCOQsyIcRjLwrr5vyF+IPZzjyIHg2vTjbNJwRbo3Q/TI9ar80zXbjAoEdnmx3Zphw/CiWOYK67FjMlA3vgn9q8XYL/wDPYvvo98+i7mollYDz2D6eu7lmT+6JcsW7KgV19Mn3jMTQswF81G3v4X9m/uhMMlWHf8EtN/sM/udyYzMtWJYeumc8cogv2v5yAsHHPNt7Fuvgu6R2M/swg5WgZ7d2IS2t+aTnV+miQrpZTyzLbNIDZm5DgATEQXrAW/BMvCfu6xoK1PFtuGgq0Yb0stTjEjxkDRNqS62seRORpqYMdPxbr1bqyfLoQukchn72FSJ2M99DTWTQswPnhgr5H6uuS8bJ9cTtxuyMtxVnRxSjrMt+7AXDQLyo9ife8uzPDRPrlXs/rEQ0wckrvx3OOyVsPWjZiv3ICJ6onpHoX1g5/DkcPYv/8V2HZQ1SOrwNEkWSmllEckdxOEhcMZWwmbXn0x826CXUXOBgzBaP9uOHkcvHho70wmeazTCq4o18eBnVKcD6FhTtKKUwZh/eoPWH94wUkse8f75bbGsjCj0nz3YOL2bVBdiUk53YfaWBbm23di/f5vmPSp7b/HORhjnNXkvOwW349UV2O/vNjpcjL9itPnDh2Buea7zu8VCJrOFiqwNElWSinlEcnLhuGjMCGhjV43E6ZBSCjy+ScBiuzcTtcjty1JJmkkuFzO+/cDKT61cUVISMNrxnI5pR7+5sWDiVJ2GPvT96j78yKqN6xuenxrFlgWjBjT6HVjDKZblM9CPqeR45z2cju3N3tY3v8PlJZg3fADjKvxRiFm1lWYiZdA3wGYnt6V5ajzU0jrQ5RSSl3o5Egp7N+NuWh2k2MmshsmdRKy5jPk2u82SaIDrmAL9IyDuD5tOt1EREJCkl/qksXthl2FmEsu8/m1PWFGj0dcLmTTmma3u5baWuSDV5Gs1c5PCwBcIRzbkQ+/earRxhaydaPzUGdkByT3LTAjxyE4CbsZ0ng1WA4fRN7/D2bipc2WfRhj4Ja7MHXt35BEnR90JVkppVSr6ldRzcixzR43U2bAieOweX1HhtUqEUHyt2CSRrWrW4FJHgs7CpAqH29RvW8X1ARu4wrTtRskjUI2rWn2uHzyNvLGCxAaivmvb2P95k9YP/stdtlh5N1/nx538jjsKGhUahEIJqoHDBjilAadRT58A0QwX/t2y+cbE3zf5KmA0SRZKaVU63I3QbfuMGBI88dHpTsdAlZ93LFxtebQfigva3M9cj0zYgycegDQl2RHvnN9H+4+5y0zbiLs24WUNG6dJiLI8g9h6Ahc9zyKdfnXMPGDMENHEDH9cuTD15GSU11Ncjc5CeiowCbJACZlHBTlNnrQUk4cQ1Z8iJl0qe8fgFTnLU2SlVJKnZOIIHnZmOSxziYUzTAuF2bSdMheh5w41rEBnoOcSmq92WmvWUNHQEgIkufjkoviAuebjwBuXFG/5bFkn7WaXJQLB/Zgps1pck63m24DVyj2y//rnLt1I3TpGhQPvJmRqeB2O2U2p8hn70NNNWbuVwMXmOp0NElWSil1bgf3wpHDzkNR52CmzoQ6N7J2eQcF5oH8LdAtCvoOaNdlTFg4JI7w+cN7UpwPCcMDunGF6dUX4gchGxsnybJsKUR0wWRMa3KOKyYOc9XXYdMaJGe90x955NgmD8MFRNIo5xuaU63gpLYG+fhtGD0e039QYGNTnYpHD+4tWLCAiIgILMvC5XKxaNGiRsfffPNNli93/lK0bZs9e/awePFiunXr1uq5SimlgpvknrseuZ4ZOAQGJDhdLmZc2RGhtUoKtjgdOXyQhJoRY5G3XkROnvBJ5wmpqoR9uzHpU9p9rfYy4yY6D+idem9ScRJZvwIzeUajh/ManTPrKmT5h9j/9ySUH8FccW0HR908Ex4OQ0c2bCoiqz+FY0ex5s4LaFyq8/G4u8X9999PVFTzLVyuvvpqrr76agDWrVvHO++8Q7du3Tw6VymlVHCTvE0Q0wt69Wt1rJkyE/n3/yL792D6tW/1tr3k0AE4dAAz0zcJu0keg7z5T8jfDGmT23/BXUXO5iwBrEeuZ8ZNRN57Bdm8HjPpUmTNMqipwUyb2/I5IaFY19+C/cffOL8+tYlIMDAjxyGv/wMpP4IsfR0GDYUR5/4mT6mz+bzcYuXKlVx00UW+vqxSSqkAELvO2UVt5DiPVmPNpEvBspDP/f8AX92fHqbulz/AfmUJsn2bs7MeIDuLsP/6JPYDd4DLhRmV7psbDhkOYWFtagUnO4ua7Ngn9b2Jg6COlyHDoXs0nOpyIcuXOg9ptrI9sxk9HtKnwIAEp2wjSNR32ZCXFzt11XPnBbSkRXVOHq8kL1y4EIA5c+Ywe3bTPpkA1dXVbNy4kZtvvtnrc5VSSgWh3cVQccLjVTgT3RNGpSOrP0XmfRNj+adGVY4fg+y1ENsbyXwL+eA1pxdyVA9n57+wcMyk6ZiZV2L6DfTJPU1oKAxL8aouWWqqkZeeR5Z9AMljsH78wOkWY8X5ENcH0z3aJ/G1h7EsZzV5/Spk+zbYVYT5xvc9Siyt7/0M6nywY58vDU6EyG7OinhMr2brqpVqjUdJ8kMPPURMTAzl5eU8/PDDxMfHk5KS0mTc+vXrSU5OblRq4em5mZmZZGZmArBo0SLi4rRFS3uEhIToZxiEdF6Ck85Ly06uWMoJIHbqdFwets6qmns15b/7NdEl+wgb3b6WYC3NTVXeRspF6PnThwgZMJjqtSuo/vxT6g6XEDH/R3SZeQVW1+7tundzTqZP5sQ/niEmxMLqEXPOse7dxZQ//ivcu7YTPukSqr9YRthLzxN1530YYzi0q4iwEaPpESS/96ounk35ig+x/vkMdlgYcVf8F1YLO+V1hj8zR8dlUP35p3SbdwNd+7RtI5nOpjPMS2fiUZIcE+P8RRAdHc2ECRMoLCxsNtFduXIl06ZNa9O5s2fPbrTKfPjwYc/fhWoiLi5OP8MgpPMSnHReWla3/nPo258jNuDhZyQJw8EVwtEVmVh927eK29Lc2KuXQWRXynv0wlRWw+gJzn9AJVBZWQ2V1U3Oay8ZOBSAw58vw5rQ/OqkiCArM5EXn4PwCKwf3Y979HhM7/5UvfUi1VE9MZd+CfvQAWqmXxE0v/ekfyKEhuHeWYSZPJ2yqhqoaj62zvBnRtKnwp6dVKRNoTLIY/WVzjAvwSY+Pr7FY63WJFdVVVFZWdnwdXZ2NoMGNW2hUlFRwdatW8nIyPD6XKWUUsFH3G4o2IpJHuPVeSYiEoaPQnL8s/ueiDjtvUaM6/iWY4OHQUQXONVerFkbPkf+7//BkOFYv37CqdsFzFXXY6bMQN78J/LvJc5rQfDQXj0THt7Q5u9cD+x1FiZ1Mq5f/9H5/ahUG7S6klxeXs7jjz8OQF1dHdOmTSM1NZWlS5cCMHeu8wdpzZo1jBs3joiIiFbPVUqpC4mIIO+/ikkYhmml13BQ2VkI1ZWYNnQFMKPHO10uSkswsb19G9eBvVB2GHPFdb69rgeMy+W8t41fIDfe1mzNtb1mGUTHYP3kwUbHjTHwrTuQssNOWzLLgkGJHRh966y5X0Vie7V7h0KlzgetJsl9+vThsccea/J6fXJcb/r06UyfPt2jc5VS6oJyYC/y6v8hxmCu+gbmyuta3LkumDR0cfByJRnAjM1wkuTsdZgZV/g2rq1Zzj0C1XIsfQqsWwGFuTB8dKNDUlMNWzZgJk9vNoE2IaFYt92Lvejn0CUSEx7RZEwgmeTRmOTRrQ9U6gIQ/H9LK6VUJyebT5UdjJuIvPlP7P/3UFBt3dwS2ZYD/Qe3rftCn/7Qqy+Ss873cW3dCL37BazlmBkzHkJCkQ2fNz2Ymw3VVZi0ljcIMV27Yf3ycawf3e/HKJVS7aVJslJK+Zls3gD9BmLd/gvMjbdD3ibsh/4b2VkY6NBaJO5aKMz1uh65njEGM3YC5GU7q6u+jGtbTkMf3EAwEZEwKg3J+hwRaXRMNq6GLpHQymqsiYjE+KH7hlLKdzRJVkopP5LqasjfjBmdjjEG69LLsO5+BBDsPy9qkmQFjeICqKluc5IMYMZkQG0NtGHzjRYVbXNWagO8u5tJnwJlh2FHQcNrYtchm9ZgxmSc7oWslOq0NElWSil/yt8M7lrM6NO7vpmEJMzVN0BpCezaHsDgWiZ52WBMqyui5zR8NISFI9m+K7mQrRudB97akbz7ghk3CVyuxiUXRdvgeDmk+mDLaqVUwGmSrJRSfiSb10NYGCQ17hZgxk4EYzVf1xoEZFsODBzSrpIAExoKI8chOet8tmIuW7MgMRkT2dUn12sr07UbJI9FNqxqeG+ycTWEhDT6hkgp1XlpkqyUUn4kmzdA8lhMaFij1033KKeXcFbwJclSWwNFee0qtahnxmY4K+b7dnt+f7cb+9N3qXvsF1Rmvn06CT1xDHYWYkamtjsuXzDpU6BkP+zd6bT5y1rt9G7uon15lTofaJKslFJ+IiX7oWRfiyuLJm0K7N+NHNjTwZG1oijPKRFJ9r4/8tnMaGeDKdncesmFiCAbVmE/8EPkhWfg4F6OPfVb7N/dhxzcB3nZIIIZFbiH9s5k0iaBMciGVbBvFxw64LymlDovaJKslFJ+Ils2AJwjSXYSKsla3WExeUK25YCxICml3dcyMXEwYEirdclycB/2I3dj/3kRWBbWHfdhPbqE7rf9HHZtx37gh9hvvghdukJCUrvj8gUT1ROGjUQ2fO7MoTFOrbJS6rygSbJSSvmJbN4Avfpiesc3e9zE9IKEpKCrS5a8HBg81Gd1v2ZsBhRuRSpOtHzPt16EPTsx37oD6/4nMeMmYiyLyLnzsB58CsZOgP27YWQAtqI+B5M+1Sm3+Ox9p1Y6umegQ1JK+YgmyUqpTkNEELsu0GF4RGprIS+71Ye4TNpk2FGAlB3uoMjOTaqroDjfJ/XI9cyYDLBtZMvGlu9bsAUzOh3r4rlNkmDTIwbXbfdg3b0I6xvf81lcvmDST20acrQUk6qryEqdTzRJVkp1CrJ3J/b9d2D/+ZFAh+KZwq1On+HR4885rH5nNtkY2JILcbuRssPImmVQ58aM8GGLtcThENkVTm0n3eTepSVOz+GzOoCczQxLwfSI9V1cPlD/0wAAo63flDqvhAQ6AKWUao296iPkhT9DTQ2U7EMqK4K+g4BsXg8hIa328zX9BkC/gU7Jxcwv+z+u2hqnPGDXdti9Hdld7HRoOHEM6tu0hYXBsPbXI9czlgtGjEW2bkREMMY0jqlgizPOBzXQgWBd/jVkSxamb/9Ah6KU8iFNkpVSQUtqqpEXn0NWfAjJYzCXXo489yjkboT0qYEO75xk8wZIGoUJj2h1rEmbjLz/H+T4Mac1nL9iqq3Bvvd7UH7EeaFLpNMLOW0yRPeE6BinpjZ+ECaii0/vbUamOt8IHNwHZyeTBblOLAMG+/SeHcWkT3Vqk5VS5xVNkpVSQUlqa7Afvdfpi3vFdZirvwEiSJdIZPOGoE5KpOwQ7NuFuWi2R+NN+hTk3X8j2Ws8PqdNCnOh/Ahm3o2YCRdDXB+M1TFVd2ZUGoKzGcjZK65SsAWGjnRWnJVSKkhoTbJSKijJ5584CfL3for11RsxLhcmJARGpiI56322g5s/SNYXwKmuDp4YNBRievm9FZzkZYNlYWZ+GdO7X4clyACmV1/o1dfZVvrMmE4cg/27O22phVLq/KVJslIq6IhtIx++DoOGOiueZzCj0+FoKezdGZjgPCAbVjklC30HeDTeGOOUPGzJQqoq/BdXXjYMGR6wem4zMhW25SBu9+kXC7c6x1p5aE8ppTqaJslKqeCTvQYO7MV86atNHvKq7xYhm9cHIrJWybEjULAFM967chCTNhnctXCONmntiquyAnYU+GQXvbYyKalQVQnF+afjKtgKIaFBs0GIUkrV0yRZKRV07PdfhdjemPEXNTlmesbCgATnwbggJFlfOFsne1szPSwFunZHNn7hn8Dyt4BtY0YGLklmxFgwVqOSCynYCglJmNDQwMWllFLN0CRZKRVUpDAXivIwc+a1uLOaGT3e2cGt0n+lCW0lG1ZB73jo712nBuNyYcZkIDnrkDrfb5gieZsgNAyGjvD5tT1lunaDhGFI7kYnpuoq2FWk9chKqaDkUXeLBQsWEBERgWVZuFwuFi1a1Oj4li1bePTRR+nduzcAkyZN4pprrgFg48aNLFmyBNu2mTVrFvPmzfPtO1BKnVfsD16Drt0x01ru8mBGj0fe/w/kboL6Hc+CgJw45uyy10yZiCdM6iRk9SdOna4Pd7wDkNxNMGwkJjTMp9f1lklJRd57xdmiemcR1NVpPbJSKih53ALu/vvvJyqq5f6dI0eO5J577mn0mm3bLF68mPvuu4/Y2FjuvfdeMjIyGDDAs4dZlFIXFjmwBzZ9gbni2nP3Fx46AiK6IJvXn94WOAjIpjVOSUMzZSIeGZUGIaHIxi98ui20HDsKe3diJl7is2u2lUlJRd55GfJykD07wJiArm4rpVRL/FpuUVhYSN++fenTpw8hISFMnTqVtWvX+vOWSqlOTJa+Dq4QzMwrzznOaQU3Dtm8Iahawcn6VRDb22np1gYmoovzvjZ+4dP3JdtynOuPCGA9cr3EZAiPQHI3IoVbYUACJrJroKNSSqkmPF5JXrhwIQBz5sxh9uymPwbNz8/nZz/7GT179uSmm25i4MCBlJWVERsb2zAmNjaWgoKCZq+fmZlJZmYmAIsWLSIuLs6rN6IaCwkJ0c8wCAViXuzKCoxlebTzWyDVlR3m8OpP6TLzSqISW+90UDH5Uo5nraZnxTFCBrctKa3ni3mxT57gUO5GIq+4hu69erX5OhXTZnL8z4/So+IYoW14X/bxY1hn7dp3rHgbVZFdiRs/CeMK/B5SR0anU5e7ibqjZXSZ/WWizvHZ699lwUnnJTjpvPiWR39bPvTQQ8TExFBeXs7DDz9MfHw8KSmnH7QYMmQITz/9NBEREWzYsIHHHnuMJ598stmVkJbq9GbPnt0o+T58+LC370WdIS4uTj/DINTR8yInT2Av/An0H4xrwS877L5tUffMIhCb6ksv8+gzkoRkAMpWfITVNbpd9/bFvNirPwW3m6qRaVS341qS6PzdeuST97G+/HXvYli+FPnbnzDzbsS68rqG1+s2roGkUZQeOdrmuHzJThrlrLoD1QMSz/nZ699lwUnnJTjpvHgvPj6+xWMelVvExMQAEB0dzYQJEygsLGx0PDIykogIZ5UqPT2duro6jh07RmxsLKWlpQ3jSktL6dmzp9dvQCnlPbFt7CVPwKEDsDULqa0JdEgtknUrYP0qzJevx/Ru+S+sM5mYOOg/GMkJjn7JsmEV9IhxygnawfSIgSHDvW4FJwf2IP/6C0R2RV7/B/br/0BEkNISOHQgOEotTjEpqad/oQ/tKaWCVKtJclVVFZWVlQ1fZ2dnM2jQoEZjjh492rBqXFhYiG3bdO/enaFDh7J//35KSkpwu92sWrWKjAwPt2lVSrWLfPAqbFoD4yZCTQ0U5nbs/Teudv7bUYCUH0Fsu/lxx8ux//ksDB6GuexrXt3DjE6HwtyAt4KTqkrYvAGTNsUnWz2b1EmwsxA5Utr6YEDctdjP/x5Cw7DufxIzbQ7yzsvIK0ucrhaAGTmu3XH5TN8B0CMWevfDROvCiVIqOLVablFeXs7jjz8OQF1dHdOmTSM1NZWlS5cCMHfuXFavXs3SpUtxuVyEhYXx4x//GGMMLpeL+fPns3DhQmzbZsaMGQwcONC/70gpheRlI6/9AzPhYsxNC7B/fAOSu6nDEiXZtwv7qd82ftHlgtHjsb55m7MhSP3YF5+DipNYP3moxb7ILTHjJiEfvIZkr8VMutQXoTchNdXYf/wNJnUSZvbVzZaMSfZaqK3xepe9lpjUSchrf0c2fYGZfkXrMb75T9hZiHXbvZiYXnDTAqdLxtLXIbIbdI+G+EGtXqejGGOwvvF9sLxvk6eUUh2l1SS5T58+PPbYY01enzt3bsPXl112GZdddlmz56enp5Oent6OEJVS3pAjpdjPPQZ9+2O+dYfTMSEx2dnl7L++1TEx7HBKsqxb7waXy1kRLTmALHsP+4E7MN/4gZPUbvwCWbscc/UNmAEJ3t9o6AiI7ols+Bz8lCSzfzfkb0byN0NpCVw3H2OdTuYlazXyt6ecrha+2hSj30Do3c8puWglSZZtOcj7r2IuntvQDs9YFtzwAwgLQ5a+7nyz1Ia+zf4UTK37lFKqOYF/zFkp5TPidmM/9yjUVGPddo+TIANmZCry9r+Qk8cxXbv7P5Dd2yEsDNImYywX9emZTL8ce8kTyOLfOzW8RXkwYAjm8mvadBtjWZi0KciqTKS6yi8dPGT/bueL9KnIR28hRw5j3fwTZ6X2zX86PX8TkpxVXMu7lfCWGGOc1eSP3kYqKzBdIpuP7eQJ7MV/gN7xmK/f0uQaXPNdTGIyDB7mk7iUUupCottSK3UekXdegsJczE0LMP1OlzaZlFQQgbzsjolj13bon9AkaTR94rF+/j+Yr30bctbByeNY3/2R0/e4jUz6FKfmevOG9obdvP17wLKwvncX5us3Q9Zq7N//CvtPDyPvvIy5aJbznmJ823bJjJsEdW5kc8sPJsqb/4RjR7Bu+Umz3yAYYzDjL8LE9fFpbEopdSHQlWSlzhNSuBV559+YKTOxzi49SEhydqjbuqntu8F5GocI7C7GTLy42ePGcmEu+xoybhIcP4oZlNi+Gw4fDd2ikPUrfVYTfCY5sAd69cOEhGJmfwXpGec8JCc25oZbMdMv908pw7ARTi3xhs9hQtPPUmwbWbcCkzoZk9B6X2mllFLe0SRZqfOAVJx0Ere43phvfL/JcRMSAsljkNyN/g/m8EGoPAmtJL+m3wDo1/4t6o3LhUmbjKxZjtTWYELD2n3NRvbvaRSnGX8RVp/+IIIZOMS39zqDsVxOKckXnyI11Ziw8MYDtm+DY0chbbLfYlBKqQuZllsodR6QF5+DU7WyLdWvmpGpcOgAcuiAf4PZvd2538D27YLnDZM+FaorYUuWT68rbjeU7HcS+jPvNyDBrwlyw33GT4Hqqmbfl2StdrbwHqNtNZVSyh80SVaqk7PXLENWf4K58jrM0BEtjqvfwKG+b66/yK7tYFnQvwNbjo0YC5HdGnZx85nDB6DO7fT1DYThY6Brd+chxzOICJL1OYwYg4nsGpjYlFLqPKdJslKdmBwpRf7xZ0hMxlzZyhbGffs7Gzhs3ejfmHZth74DmpYH+JEJCXG6QWxag7hrfXfh/Xuc6/cLTH93531NRDatRWrPeF/7djm76KVpGzWllPIXTZKV6sRkw+dQeRLr2z9sdSMOYwwmJdXZaKSF3e98Yvf29j+M1wYmfapTC53ruw4ecsBJkgO2kswZ7yvv9E8AJGs1GIMZNzFgcSml1PlOk2SlOrOiXGd12NOVzpHj4OTxhrphX5NjR+FoGQzs+CSZlFSng8f6lb675v7d0COmxTrvDjEyFbpENiolkazVzk8PesQELi6llDrPaZKsVCcm27fB0GSPW5CZFGdbatnqp7rkXace2gvESnJoKGbsRGTjF84Ddz4g+/d4/g2Inzjva0LD+5LSEthVhNGuFkop5VeaJCvVScnRUigtwSS2/LDe2UxUT+g/2G+t4GR3sfNFIFaSwemTfPI4FGxp97VEBA7swfTt74PI2sekn3pf+ZudraoBk6pJslJK+ZMmyUp1VkXbAM7Z0aI5JiUVCrYilRW+j2n3dojtjenazffX9kTKOLAsJC+n/dc6WgZVlQFfSQZgVDqEhSMbVjmlFvGDMH3iAx2VUkqd1zRJVqqTku15EBICg7zrR2wmXAzuWmT1J76Padf2VjcR8ScTEQmDhiL5m9t/sf27nWsG8KG9eiY8HDMmA1m3EvK3aKmFUkp1AE2SleqkpCgPBg/DhIZ6dZ4ZMhwSkpCP33FKCnwVT1UllOzDBKjUop4ZPhp25CM11R6Nl6pKaovymr5e39nCB7sC+kR9KYnYmiQrpVQH0CRZqU5IamthZ6HXpRb1zIwr4cAeyPNduzT2FDtbNQdwJRnAJI8Gt9vZtrkFYtvIts3Y//sE9k+/TdlP5yNbz9rVbv8e6BIJ0cHRQcKMGQ8hoRDTy+ufHiillPKeJslKdUa7isDtbnuSPGEadIvC/vgdn4UkpzpbBOqhvQbDUsBYLZZcSPZa7F/+APvxXyBZn2MmXoIV2wv7nZcbj9u/29kUxcPOIf5mIiIxX70JM+/GoIlJKaXOZyGBDkCpQJOTJ5APXoUzd2ozBnPRbEx8B26t7AWpLw/worPFmUxoGObiucj7ryKlhzCxvdof1K7t0C0Kesa2/1rtYCK7wsAhyLamSbKIYL+0GCwLc/N/Y9KmYsLDifz8I47/7x+Rgq2YpBRn8IG9DVt5Bwtr7rxAh6CUUhcMXUlWFzxZ/gHy3ivIsqWn/8t8E/sfT/u0ZteXZHue00WiHZtJmEsvd6712Xu+iWl3MQxKDIpVTjN8NGzfhtTWND6wa7tTN33Z17Amz8CEO1tnd5n7Fegejf2us5osFSehvCw4OlsopZQKCI9WkhcsWEBERASWZeFyuVi0aFGj48uXL+eNN94AICIigltuuYWEhASPzlUq0GTtChgyHNcvHm94zf7kHeSfzzo1uyPHBTC6pkQEivKcRLAdTGwvSJ2ILF+KXHU9JjSs9fse3IvkZiN5m5yE88xvIsoOY4JkpdMkj0Yy34DifDjjc5K1y8DlwqRPaTw+PAIz+2rktb8jOwudmmbABMtDe0oppTqcx+UW999/P1FRUc0e6927Nw888ADdunUjKyuL5557jt/+9rcenatUIMnBfc7uZdfOb/S6mTYHefcV7DdfxBoxtsNWR6Wqwmljdi5lh50evm0stTiTNeNK7KzVyNoVmKkzWxxnr/oIee0fcLTUeSG2NyYx2XmQrJ7LhZk2p90x+UTSKDAGyd/c8M2E2LbzDVFKGqZr9yanmOlXIO+/iv3uK5ixE5wXg6D9m1JKqcDwSU1ycnJyw9dJSUmUlpb64rJK+Z2sXQ6Aybio0esmNAxzxTUdupos+Vuw//ArzI0LsC6a1fK47U49shnW/iSZEWOh30Dkk3eghSRZ7DrkP/8H3aMxV30dM2Ic9OobFGUVLTFdu0H/BCT/jJ33tudB2SHMV29s/pzIrpiZVyLv/hupczs9qHv17aCIlVJKBRuPa5IXLlzI3XffTWZm5jnHffzxx6SlpbXpXKU6mqxbAcNSMDFNH1wz0+ZAj1jsN1/0e22yiGC/+n/gdiP//l/k+LGWBxflQVgY9E9o932NMU47uB0FSHFB84O258Oxo5jLr8G65DJM735BnSDXM8mjoSgXOfVApqxZDqFhmHGTWj5n1tUQGgab1kDveIzL1VHhKqWUCjIerSQ/9NBDxMTEUF5ezsMPP0x8fDwpKSlNxm3evJlPPvmEBx980OtzMzMzG5LoRYsWERcX19b3pICQkBD9DFvh3l1M6d6ddP/eT4hs4bOquPY7HP/L74jav5PwsRntvmdL81K9diVHi/KIvOrrVLz7CmHv/IvoO37R7DVKdxZihqUQ09c3q5z2lV/j0CtLCN+wgqgJU5ocP/72JipCQoib/iWsQG033QZVGVMp/+gtoo8cInR4Coc3rCIsYyo9BjbtWNIwL3FxHP/SPCreeonwwYn00D9DAad/lwUnnZfgpPPiWx4lyTExzhP00dHRTJgwgcLCwiaJ7s6dO3n22We599576d69u1fnAsyePZvZs2c3/Prw4cPevxvVIC4uTj/DVthL3wJjcTJ5HBUtfFaSNhV6/JWjf38G6+f/0+4V1ObmRWwb+29PQe9+VF3xdUytm6r3/0NNxjTMsMZ/VqSmGrs4HzN3nk/n16RNpnLZh1Rf/c1GD/CJCPaqj2HEWMoqq6Cyymf39Dfp43SmOLpmBebwIezyI9SOm9Ts53bmvMgll8EHr1PTd6D+GQoC+ndZcNJ5CU46L96Lj49v8Vir5RZVVVVUVlY2fJ2dnc2gQY1XYg4fPszjjz/OHXfc0ehmnpyrVCCIiFOPPHwUJrpni+NMaCjmimuhcKtvd6c7M5a1y2HPDszVN2BCQjBf/jrE9ML+x5+RU10WGuwohLo6jA8e2juTmToTKk44ZQZn2rsTDh3olNsgm+5R0H8wkr/F+YzDu8Do1n8aYHrEYi18BjNnnv+DVEopFbRaXUkuLy/n8ced1lh1dXVMmzaN1NRUli5dCsDcuXN55ZVXOHHiBM8//zxAQ6u3ls5VKuB2F8PBvZg5X2l1qNPp4t/Yb7+Ey8cP8InbjbzxAgxIwEy42LlfeATWN76H/dRvkY/fwsz9qjNWBNmW45zYxp32WjRiLPSMw171Ma6Maafjy1rtbKxyjjreYGaGj0JWfYzsKMCkTmzoi9zqeT0CuyGKUkqpwGs1Se7Tpw+PPfZYk9fnzp3b8PWtt97Krbfe6vG5SgWarFvu7LqWPrXVsSY0FHPJl5A3/4kcO4KJannl2es4VmbCoQNYd/wKY53xg51xk2DsBOTNF7F79oLCrUjOOjh0AAYOwXSP9lkMAMZyYabMQN77D3K0rGGTEtm4GhKTz7naHsxM8hjkk3edrydeEuBolFJKdSa645664DilFitg5DjnR/IeMOOcvrmSs953cdRUI2//y1kVPuuhQGMM1vXfA7GR5x5FViyFvgMwN9yK9ePf+CyGRvecMtO53xefOvEdPgi7tmPSmj7M12kkjXL+H9kNgmyLaaWUUsHNJ32SlQoUsW04UursHuepHYVw+KBT++upgYnQIxbZtAYumt36eA/IS8/D0TKs7/2s2QcCTa++WP/9EFRVwPDRmDDPSgXayvTtD0NHICs/QuZ+1VlFhk5Zj1zPRPWA5DGYhGGYMzc+UUoppVqhK8mqU5M1n2H/4nvI7mLPxh87gv3CnyEkFJPqefJnjHF2Ydu6EamtbWu4DexVHyPLPsBcfg1m+KiW7ztsJGb0eL8nyA33mzoL9u+GHYVOPXL/wZje/Trk3v7i+ulCrGu+G+gwlFJKdTKaJKvOLX8L2Dby4RutDpX9e7B/+zPYvwvr1rudXdm8YMZNgOoqyN/c1midOPbsQF542lkd/so323UtXzMZ0yA0DPngVSjI7dylFkoppVQ7aJKsOjXZ4ewSJ2uWIUfLWh6Xvxl70c+hphrrp/+DGTfR+5uNGAthYU7JRRvZlSexn3kEunTF+v7Pgm5HNxPZFZM2BVm/EsTGpHXOrhZKKaVUe2mSrDotqamGvTud1ml2XUMXgybj1q3A/sOvISoa697HMEOS2nQ/ExYOI8Yh2WvbtE21iHDsqUVQst+pQw7SjhFm6kzni9jeTi22UkopdQHSJFm1iYggpSXIwX2n/zt8sGOD2F0Mtu0kyamTkM/eQ6qrG8e5uxh78e9h8DCsux/B9GrfVs5m3AQoLYF9u706T2wbeeMFqld+hPnqjZjk0e2Kw69GjoV+AzFTZ7Z7h0GllFKqs9LuFqpNZN0K5LmmPbDNvBuxrryuY2I4VWpBQhJWtyjsrNXI5x9jpl/uHK+pxn7+d9C1O9aCX2K6edbu7VzMmAkIINlrMf092z1Sjh/D/t/fw+YNRMy4gpov/Ve74/AnY7mwfvMnTZCVUkpd0DRJVm2z4XOI6oG5dn7DS7L6E+TdfyPT5nRMKcGOAoiOwfSMRXrEQEISkvkmcsmXMJaFvPo32LcL60f3+2zzDdMzFgYlItlr4PKvtTpeCrdiP/c4HC/H3Hg7Uf/1TUpLS30Siz9pgqyUUupCp+UWymtSV4dszcKMycCaPP30fzf8AOrczgYZHRHHjgJIGAacatE25ytwcC/krEM2b0A+egsz88uY0eN9el8zdiIUbUOOHzvnOPuTd7Ef+wWEhGDd+yjWpZdp8qmUUkp1EpokK+9t3wYVJzFjGiefpnc85uK5yPKlSMk+v4YgFSfhwF5MwumH8Ez6VIiJw37nZey//hHiB2G+9m2f39uMneDsTLel5d33pKrC2SxkxDis+/6AGTTU53EopZRSyn80SVZek5x1YFkwMrXJMfPl68EVgrz+gn+D2Fno3O/MJDkkBDPrKijOh5PHsW65yz+bcAweCtE9YdPalsds3Qh1bqwrr8VEdvV9DEoppZTyK02SldckZz0MS2k2+TPRPTFzvoKsXY6cSmT9EsOOU9c+VW7RcP9pc51d4r5+C2bgEL/c21gWZkwGsmUD4m5+9z3JXguRXWHoSL/EoJRSSin/0iRZeUWOlMKe4nPW+Zov/Rd064796t/8F8eOAujVt0nHChPZFdcD/w9r+hV+uzecKu2orIDspqvJYttI9jrMqPSg2yxEKaWUUp7RJFl5RTY7dbhn1yOfyXSJxFxxHWzdiGzd6J9AdhQ0KrXocCmpEB2Dverjpsd2FsHxchg7ocPDUkoppZRvaJKsvCKb10PPOOg/+JzjzPQrILY39jsv+z6GY0eh7FCTUouOZFwuzJQZTieN8iONjkn2WjAWZnR6gKJTSimlVHtpkqw8Ju5a2LoRMzq91VZmJjQUk3ERbM9Dapuv222zU5uIBHQlGTBTZ4FtI6s/bfS6ZK+Fock+2bxEKaWUUoGhSbLyXFEeVFVixmR4NNwkJoPbDbuKfBqG7CgAY0GA26qZfgMgMRlZ9REi4sR2tBR2FTlt4pRSSinVaWmSrDwmOevAFQIjx3p2QmKyc17xNt/GsaMQ+g3ARHTx6XXbwlw0C/btglPdNiTnVM22JslKKaVUp+bRttQLFiwgIiICy7JwuVwsWrSo0XERYcmSJWRlZREeHs7tt99OYmIiABs3bmTJkiXYts2sWbOYN2+ez9+E6hiSsx6SUjARkR6NNz1iIaYXFG2D2T6KQcR5aM/D1Wx/MxkXI/96Hln1EWZIklNqEdML4gcFOjSllFJKtYNHSTLA/fffT1RU8zWWWVlZHDhwgCeffJKCggKef/55fvvb32LbNosXL+a+++4jNjaWe++9l4yMDAYMGOCzN6A6hpQegn27nJVTL5ihI5CiPN8FUnbI6RwR4HrkeiayKyZtCrLmM+SrNzk121Nn6fbTSimlVCfnk3KLdevWcckll2CMYfjw4Zw8eZIjR45QWFhI37596dOnDyEhIUydOpW1a8+xS5kKWqdbv3m5gps4HMoOObW67Y2hqhL7H3924kgKnk06zEUzoeIk8vJiqKnWUgullFLqPODxSvLChQsBmDNnDrNnN/7ZeVlZGXFxcQ2/jo2NpaysjLKyMmJjYxu9XlBQ0N6YOy3J34L94nNg151+0XJhfeP7mOGjAheYJ/KyndZvfb37KYAZkowAbN8G6VPbfHspP4L95IPORiY33Y4Z4J/d9NpkxFiIiUNWZkJYOIwYE+iIlFJKKdVOHiXJDz30EDExMZSXl/Pwww8THx9PSkpKw/H6J/vPZIxp8fXmZGZmkpmZCcCiRYsaJd3niyNPvUbt0VLCxpzun1uzZSOud/5FzNQ/+/ReISEhPv0MD+0oICxlHD169fLqPImeSElIKBH7d9O9jfG4d+/gyCN3Y46XE/2LRwkf3/Zk219OzLySk6/8H2HjJtCzX3yL43w9L8o3dF6Cl85NcNJ5CU46L77lUZIcExMDQHR0NBMmTKCwsLBRkhwbG8vhw4cbfl1aWkrPnj1xu92UlpY2eb05s2fPbrRCfeb1zgdycB/2xjWYr9yA+8vXnz6Q+Sa1Lz3PoS9WYIaO8Nn94uLifPYZypFS7MMHqZn15bZdc1AiFZuzqG7DuZK/BfuphRASgvXThRwfPIzjQfh7Q9KmwusvUDtmwjk/I1/Oi/IdnZfgpXMTnHRegpPOi/fi41te2Gq1JrmqqorKysqGr7Ozsxk0qPGT+xkZGSxbtgwRIT8/n8jISHr27MnQoUPZv38/JSUluN1uVq1aRUZGcHQl6Giy7H1wuTDT5jZ63Vw8F7p2x37/PwGKzAPbnQfvTGLbkniTOAJ2FiJut1fn2WtXYP/hVxAVjXXPo5jBgdthrzWmdz+sR/+KmTw90KEopZRSygdaXUkuLy/n8ccfB6Curo5p06aRmprK0qVLAZg7dy5paWls2LCBO++8k7CwMG6//XYAXC4X8+fPZ+HChdi2zYwZMxg4cKAf305wkppqZEUmJm0KpkdMo2MmPAIz40rk7X8h+/c4G1QEGSnMg5BQGJTYtgskJkPmG7Cn2KOuFCKCfPg68u8lMCwF645fYrp2b9u9O5DprjvsKaWUUueLVpPkPn368NhjjzV5fe7c0yuixhhuueWWZs9PT08nPT292WMXClm7AipOYKZf3uxxM/NKZOmryAevYr5zp//jqayArVmQPtWjVmWyPQ8ShmFCQtt0P5PoPLwn27e1upW02HXIS4uRj9/GjL8Ic/N/Y0LD2nRfpZRSSqm20h33OoB8+i70GwjDRzd73HSPxlw0G1n9KXKk/a3SWo3n7Zewn3kEefVvrY+trXW2WW5jqQUAMXHQI8bZVKS1+/3vE06CPOcrmO//TBNkpZRSSgWEJsl+JjsKnB3ipl9+zlVbM2ceiI1kvunfeESQrM8hNAx5/z/Yrd1vVxG43ZihyW2+pzEGEpNb3Z5aTp5AvvgMM+sqrOtuxlj621MppZRSgaFZiJ/Jp+9BeARm8oxzjjO9+mIypiHL3kcqTvgvoH274NABzLXfhbTJyMuLsdcub3F4w2557VlJ5tTDe4cOIMeOtjxozw5n7OgLuzxHKaWUUoGnSbIfyckTyJplmEnTMZFdWx1vvvRfUFWJfPa+/2LK+hyMwaRNwfreT2HYSGTxH5DcTc2P354Hsb2bPHDoLZN4aiV6e8urybJ7u/PFwDY+IKiUUkop5SOaJPuRrPoIamto6YG9s5lBiZA8BlmR6b+YslZDYjKmRwwmNAzrjvugb3/sp3+L7N3VeKwIFOX5pn/z4KHgcjlJd0v2FEP3aEx08720lVJKKaU6iibJfiSrPoIhwzEDPd9C2aRNgZJ9SMl+38dTWgK7tmNSJ52+X2Q3rB89AJYL+7WzHuQrOwxHy9pdagFgwsJhwBBke37L8e3eAV58VkoppZRS/qJJsofErkOOH/N8/J4dsGcHZsq5a5HPZk5tWS2b13t1nkcxZa127pE2pfE9e8Zi5nwFNq1BdhWdHl+/iUg7HtprdJ/EZCjOb3ZTEXG7Yd9Or76hUEoppZTyF02SPSQfvIZ917ewX3gGOdF6sixffAaWhcmY5tV9TO946B2P5PgpSY4fhOnTdAtGM/PL0KUr9lsvnX6xKA/CwmCAbxJXM3wU1FTDzsKmBw/uBbfbZ/dSSimllGoPTZI9JF98Bl27IZ+9j33fbdifvovYdc2PtW1n/Kh0TPdor+9lxoyHbTlITXV7wz4d0/FyKNiKSZ3c/D0ju2JmXwUbVyO7i51ztm+DhCRMSKt7zngmeYxz3W05TeM7dU9dSVZKKaVUMNAk2QNSsg/27sRceS3Wr5+AAQnIC89gP/QTp873bAVb4MhhzOTpbbqfGT0eamtg2+Z2xX0myV4LYmPSm0+SAcysqyGiC/Y7LyG1NU79sg/qkRuu3z0a+g9uNklmd7Gz9XWf/j67n1JKKaVUW2mS7AHJ+gIAkzoZMyAB666HsW69Gw4dwH7hmabjv/gMwrtgxk1qcswjyaMhLAzJWdeesBvHlLXa2flu0NAWx5iu3TAzr4L1q5DPP4a69m0i0uw9ksdA4VZnJ78z49tT7JSC+GrVWimllFKqHTRJ9oBkfQ6DEjFxfQBnBzkz/iLM1ddDzjpk09rTY2trkHUrMemTMeHhbbqfCQ2DEeOQzeudNmztjb+qErZkOUn+OXb9AzBzrobwLshLi50XfLiSDKeS5JoaKD7d5UJEYHcxZmCCT++llFJKKdVWmiS3QsqPwPZtzdbymplXQb+B2C/9xSlPAMheB5Un21xq0XDt0ePh0AE4uK9d14FTpRbuWkxay6UWDfftFoWZeYXzgF2vvpioHu2+fyPJo8GYxiUX5UfgeLluIqKUUkqpoKFJcitk4xcg0myCaUJCsK7/nrPd8gevAWCv/hSie8KIse26b/3WzLK5/SUX8um7ENcHho/y7N5z5jlbaQ9Lafe9m1y7a3cYOKRxkrzn1EN72tlCKaWUUkFCk+RWSNbn0Ksv9B/c7HGTkgrjpyLv/dvp0LB5HWbCJRjL1a77ml59od/AdreCk11FTleLGVd6HJPpHo31i8cx185v171bvH7yGCjKa1h9l907nAMDEvxyP6WUUkopb2mSfA5ScRLycjBpU85Zy2tdezMA9h9+DW53u0st6pnR6ZC/GamuavM15OO3ISwcM222d/eOH4TpHtXm+57z2sljwV3r9GEG2L0dYnphunbzy/2UUkoppbylSfI5SM46p8NDK7W8JrYX5orrnLravgNgkG9qa82YDGeDjbzsNp0vx8uRL5Zhps7ERAZRApqUAsZqKLmQPTt0O2qllFJKBRVNks8la7VTX5zYehs0M3ceDB+FmTuv1Q4SHhuW4nSaaGMrOFn2gfPA3owrfROPj5jIrpAwDMk7tWHKgb26iYhSSimlgoo2pW2B1NYgmzdgJl2KsVr/XsKEhuH62f/4NAYTGgojxyKbNyAiXiXf4nYjn74HKamY+EE+jcsXTPIY5MM3oLjA2eREH9pTSimlVBDxOEm2bZt77rmHmJgY7rnnnkbH3nzzTZYvX94wbs+ePSxevJhu3bqxYMECIiIisCwLl8vFokWLfPsO/CV7LVRXetQ2zZ/MmPFOh439u6GZZFdsG/nbn2DgEMyMKxoezqv+YhkcLcW68faODtkjJnkM8v5/kE/ecV7QlWSllFJKBRGPk+R3332X/v37U1lZ2eTY1VdfzdVXXw3AunXreOedd+jW7XQN7P33309UlH8eAvMHKT+C/c9nod9AGDEmoLGY0eMRQDavb35FuDgfWZkJgKxdjvXtOzH9BlDxzr+drhxj0js2YE8NGwkul9M9JLyL06JOKaWUUipIeFSTXFpayoYNG5g1a1arY1euXMlFF13U7sACRew67L88DlUVWD+4GxMSGtB4TEwv6D+4xVZwkr0WLAtzw62wfw/2gz/C/uez1OZu8qrtW0czEV0gIQlsGwYM9qikRSmllFKqo3i0kvzXv/6VG2+8sdlV5DNVV1ezceNGbr755kavL1y4EIA5c+Ywe3bzrcgyMzPJzHRWRBctWkRcXJwnofnciRef5+S2HKJ++Eu6jAuOVdjjE6dR8dZLxHTtgtWla6NjpVs2YFJSibn2W9TNvpLjz/2O6k/ewUR0Ie7q67C6dg9Q1K07kTaJk0V5dElKISpA893RQkJCAvZ7W7VM5yV46dwEJ52X4KTz4lutJsnr168nOjqaxMREtmzZ0urY5OTkRqUWDz30EDExMZSXl/Pwww8THx9PSkrTndxmz57dKIE+fPiwN+/DJ2RrFva/l2CmzuLk2EmcDEAMzZGho8DtpnTFJ41qpKW0BHtnEeba+ac/r5t/gpUxjajoaMoqq6GyOkBRt04GDQOgqldfaoLks/a3uLi4gPzeVuem8xK8dG6Ck85LcNJ58V58fHyLx1pNkrdt28a6devIysqipqaGyspKnnzySe68884mY1euXMm0adMavRYTEwNAdHQ0EyZMoLCwsNkkOdDkaCn287+HvgMwN/wg0OE0NnQEdIlEctY1TpI3rQHAjJvYaLgZN5HwuDiOB/sflOQxmJtux0y8NNCRKKWUUko10mqSfMMNN3DDDTcAsGXLFt56661mE+SKigq2bt3KD3/4w4bXqqqqEBG6dOlCVVUV2dnZXHPNNT4M3zdExEmQq6uwfroQEx4R6JAaMSEhMDIVyVnfqBWcbFoLfftj+rT8XVAwM5aFueSyQIehlFJKKdVEm/skL126FIC5c+cCsGbNGsaNG0dExOkEs7y8nMcffxyAuro6pk2bRmpqajvC9Q9jDNaXvopUVgRlT2E41QpuwyrYuxMGJCCVFbAtBzPrqkCHppRSSil13vEqSR41ahSjRo0CTifH9aZPn8706dMbvdanTx8ee+yx9kXYQcyYDHy0T55fmNHpTiu4nPWYAQmwNcvZMnvchECHppRSSil13tG+W52E6RELA4cgm50tqmXTGojsBkNHBjgypZRSSqnzjybJnYgZkwGFucjJ486K8pjxGFdw9kFWSimllOrMNEnuRMzo8WDbyNsvwYljMFZLLZRSSiml/EGT5M4kMRkiuyIfvw0uF2Z0cGx2opRSSil1vtEkuRMxLhdmVLqzlfOwFExkt9ZPUkoppZRSXtMkubM5tXp89gYiSimllFLKd9rcJ1kFhkmfCruLMVNmBDoUpZRSSqnzlibJnYyJ6IL5+i2BDkMppZRS6rym5RZKKaWUUkqdRZNkpZRSSimlzqJJslJKKaWUUmfRJFkppZRSSqmzaJKslFJKKaXUWTRJVkoppZRS6iyaJCullFJKKXUWTZKVUkoppZQ6ixERCXQQSimllFJKBRNdST5P3XPPPYEOQTVD5yU46bwEL52b4KTzEpx0XnxLk2SllFJKKaXOokmyUkoppZRSZ9Ek+Tw1e/bsQIegmqHzEpx0XoKXzk1w0nkJTjovvqUP7imllFJKKXUWXUlWSimllFLqLCGBDkB55umnn2bDhg1ER0fzu9/9DoAdO3bwl7/8haqqKnr16sWdd95JZGQkJSUl/Pd//zfx8fEAJCUl8f3vfx+A7du389RTT1FTU0NaWhrf/e53McYE7H2dD3wxN9XV1fz+97/n4MGDWJbF+PHj+eY3vxnIt9Xp+erPTL1HHnmEkpKShmuptvHVvLjdbhYvXszWrVsxxnD99dczefLkgL2vzs5X87JixQpee+01jDH07NmTH/7wh0RFRQXsfXV23swLwM6dO3nuueeorKzEGMP//M//EBYWpv/2t5WoTmHLli1SVFQkP/nJTxpeu+eee2TLli0iIvLRRx/Jiy++KCIiBw8ebDTuTPfcc49s27ZNbNuWhQsXyoYNG/wf/HnOF3NTVVUlOTk5IiJSW1srv/rVr3Ru2slXf2ZERFavXi1PPPHEOccoz/hqXl566aWGcXV1dVJeXu7nyM9vvpgXt9stN998c8Nc/P3vf5eXXnqpA6I/f3kzL263W+666y4pLi4WEZFjx45JXV1dwzn6b7/3tNyik0hJSaFbt26NXtu3bx8jR44EYOzYsXzxxRfnvMaRI0eorKxk+PDhGGO45JJLWLt2rd9ivlD4Ym7Cw8MZPXo0ACEhIQwZMoTS0lL/BHyB8MW8AFRVVfH222/zta99zS9xXmh8NS+ffPIJ8+bNA8CyLF2tbCdfzIuIICJUV1cjIlRUVBATE+O3mC8E3szLpk2bGDRoEAkJCQB0794dy7L03/520CS5Exs4cCDr1q0DYPXq1Y2SqpKSEn7+859z//33k5ubC0BZWRmxsbENY2JjYykrK+vYoC8Q3s7NmU6ePMn69esZM2ZMh8V7oWjLvPzrX//iqquuIiwsrMPjvVB4Oy8nT54E4KWXXuLuu+/m97//PUePHu3wuM933s5LSEgI3/ve9/jpT3/KD37wA/bu3cvMmTMDEvv5rKV52b9/P8YYFi5cyN13380bb7wB6L/97aFJcid222238cEHH3D33XdTWVlJSIhTYt6zZ0+efvppHn30Ub797W/z5JNPUlFRgWgjkw7j7dzUq6ur449//COXX345ffr0CVT45y1v52XHjh0cOHCAiRMnBjjy85u381JXV0dpaSnJyck88sgjDB8+nL///e8BfhfnH2/nxe12s3TpUh555BGeffZZBg0axGuvvRbgd3H+aWle6urqyMvL44c//CEPPvgga9asIScnR//tbwd9cK8T69+/P/fddx/g/Phlw4YNAISGhhIaGgpAYmIiffr0Yf/+/cTGxjZaCSgtLdUfhfmJt3MzdOhQAJ599ln69u3LlVdeGZjAz3PezktRURHFxcUsWLCAuro6ysvLeeCBB3jggQcC9RbOS97OS2JiIuHh4Q3fvEyePJmPP/44MMGfx7ydl/pkrG/fvgBMmTKlYTVT+U5L8xIbG0tKSkpD6VFaWhrFxcVcfPHF+m9/G+lKcidWXl4OgG3bvPrqq8yZMweAY8eOYds2AAcPHmT//v306dOHnj170qVLF/Lz8xERli1bRkZGRsDiP595Ozfg/Fi/oqKC73znOwGJ+ULg7bzMnTuXZ599lqeeeooHH3yQ+Ph4TZD9wNt5McYwfvx4tm7dCsDmzZsZMGBAYII/j3k7LzExMezZs4djx44BkJ2dTf/+/QMT/HmspXkZN24cu3btorq6mrq6OnJzcxkwYID+298OuplIJ/HEE0+wdetWjh8/TnR0NNdddx1VVVV88MEHAEycOJEbbrgBYwyrV6/m5ZdfxuVyYVkW1157bcMfiKKiIp5++mlqampITU1l/vz52gamnXwxN6Wlpdx2223079+/4Udnl112GbNmzQrkW+vUfPVnpl5JSQmPPPKItoBrJ1/Ny6FDh/jTn/7EyZMniYqK4vbbbycuLi6Qb61T89W8LF26lPfeew+Xy0VcXBwLFiyge/fugXxrnZo38wKwbNkyXn/9dYwxpKWlceONNwL6b39baZKslFJKKaXUWbTcQimllFJKqbNokqyUUkoppdRZNElWSimllFLqLJokK6WUUkopdRZNkpVSSimllDqLJslKKaWUUkqdRZNkpZRSSimlzqJJslJKKaWUUmf5/51oGy6Q+1a2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Taking a log transform\n",
    "ts_log = np.log(ts)\n",
    "plt.plot(ts_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "id": "OX-71KCDkvDR",
    "outputId": "00e7e5d2-a754-40db-def1-ace0ebda92da"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa9f9056ad0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exponentially weighted average to smoothen out the curve\n",
    "exp_weighted_avg = ts_log.ewm(halflife = 12).mean()\n",
    "plt.plot(ts_log)\n",
    "plt.plot(exp_weighted_avg, color = 'blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3bzbY4j1kvIL",
    "outputId": "cc0a6ff9-af14-4d03-abdc-96951be5890b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -3.601262420161675\n",
      "p-value : 0.0057369388015119215\n",
      "#Lags Used : 13\n",
      "Number of Observations Used : 130\n",
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "#Getting the residual so as to understand whether exponentially weighted average could understand the trend or not\n",
    "#by performing dickey fuller test\n",
    "ts_log_ewm_diff = ts_log - exp_weighted_avg\n",
    "adfuller_test(ts_log_ewm_diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "Lv_QsrEBeCMh",
    "outputId": "a2124992-665b-4a4b-b872-fefd56262742"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#a one shift difference for ts_log just as an attempt to check another stationarity\n",
    "from statsmodels.tsa.statespace.tools import diff\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "plt.plot(diff(ts_log),color='b');\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Difference in log value of passengers')\n",
    "plt.title('Differenced series')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NsoDIIdqvCh3",
    "outputId": "f0a8714e-c9c4-47f4-ff60-5b9322f33407"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ADF Test Statistic : -2.7171305983881595\n",
      "p-value : 0.07112054815085424\n",
      "#Lags Used : 14\n",
      "Number of Observations Used : 128\n",
      "strong evidence against the null hypothesis(Ho), reject the null hypothesis. Data has no unit root and is stationary\n"
     ]
    }
   ],
   "source": [
    "#Dickey Fuller test for one shift\n",
    "adfuller_test(diff(ts_log))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "T9ZCroB1ep1i",
    "outputId": "330359a3-fb0f-4511-a803-97c8828a6428"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages/statsmodels/graphics/tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot PACF and ACF to obtain orders of AR and MA values.\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "plt.figure(figsize=(16,4))\n",
    "ax1 = plt.subplot(1,2,1)\n",
    "plot_acf(diff(ts_log), title='ACF of differenced time series',ax=ax1 )\n",
    "plt.xlabel('Lag')\n",
    "plt.ylabel('ACF')\n",
    "ax2 = plt.subplot(1,2,2)\n",
    "plot_pacf(diff(ts_log), title='PACF of differenced time series',ax=ax2)\n",
    "plt.xlabel('Lag')\n",
    "plt.ylabel('Partial ACF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KhNfd44jnaLG"
   },
   "source": [
    "### Using widely used difference operation and hence We can think of three cases taking AR(1) or MA(1) or ARIMA(1,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 727
    },
    "id": "VrtcYjbqep5k",
    "outputId": "bf9fbec0-1fea-4ec8-a405-62626ec5b46e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n",
      "/Users/liguanghui/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages/statsmodels/tsa/base/tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "fit() got an unexpected keyword argument 'disp'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_63863/2699835324.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      6\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mstatsmodels\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtsa\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marima_model\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mARIMA\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      7\u001B[0m \u001B[0mmodel1\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0msm\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtsa\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marima\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mARIMA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mts_log\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 8\u001B[0;31m \u001B[0mmodel1_fit\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel1\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisp\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      9\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel1_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msummary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     10\u001B[0m \u001B[0mresiduals\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mDataFrame\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel1_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mresid\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mTypeError\u001B[0m: fit() got an unexpected keyword argument 'disp'"
     ]
    }
   ],
   "source": [
    "#Analysing MA(1) Model and plotting its graph \n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "model1 = sm.tsa.arima.ARIMA(ts_log, order=(0,1,1))\n",
    "model1_fit = model1.fit(disp=0)\n",
    "print(model1_fit.summary())\n",
    "residuals = pd.DataFrame(model1_fit.resid)\n",
    "model1_sse = sum((residuals**2).values)\n",
    "model1_aic = model1_fit.aic\n",
    "plt.plot(diff(ts_log))\n",
    "plt.plot(model1_fit.fittedvalues, color = 'blue')\n",
    "plt.title('RSS: %.4f'% model1_sse )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 727
    },
    "id": "3FQho-4fgVoD",
    "outputId": "45802a17-466b-43be-96e8-2212362c5fbb"
   },
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "\nstatsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\nbeen removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .\nbetween arima and model) and statsmodels.tsa.SARIMAX.\n\nstatsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\nis both well tested and maintained. It also offers alternative specialized\nparameter estimators.\n",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNotImplementedError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_59981/3152300867.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m#Analysing AR(1) Model and plotting its graph\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mmodel2\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mARIMA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mts_log\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0mmodel2_fit\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel2\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisp\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel2_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msummary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mresiduals\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mDataFrame\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel2_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mresid\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     43\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     44\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 45\u001B[0;31m         \u001B[0msuper\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     46\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     47\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/opt/anaconda3/envs/py37-pytorch/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py\u001B[0m in \u001B[0;36m__init__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     27\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     28\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0m__init__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 29\u001B[0;31m         \u001B[0;32mraise\u001B[0m \u001B[0mNotImplementedError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mARIMA_DEPRECATION_ERROR\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     30\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     31\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNotImplementedError\u001B[0m: \nstatsmodels.tsa.arima_model.ARMA and statsmodels.tsa.arima_model.ARIMA have\nbeen removed in favor of statsmodels.tsa.arima.model.ARIMA (note the .\nbetween arima and model) and statsmodels.tsa.SARIMAX.\n\nstatsmodels.tsa.arima.model.ARIMA makes use of the statespace framework and\nis both well tested and maintained. It also offers alternative specialized\nparameter estimators.\n"
     ]
    }
   ],
   "source": [
    "#Analysing AR(1) Model and plotting its graph \n",
    "model2 = ARIMA(ts_log, order=(1,1,0))\n",
    "model2_fit = model2.fit(disp=0)\n",
    "print(model2_fit.summary())\n",
    "residuals = pd.DataFrame(model2_fit.resid)\n",
    "model2_sse = sum((residuals**2).values)\n",
    "model2_aic = model2_fit.aic\n",
    "plt.plot(diff(ts_log))\n",
    "plt.plot(model2_fit.fittedvalues, color = 'blue')\n",
    "plt.title('RSS: %.4f'% model2_sse )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 761
    },
    "id": "yu2lcdVCgVlb",
    "outputId": "baeb2cb4-f505-4e45-9105-14f277100eeb"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ARIMA' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_59981/3415417674.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m#Analysing ARIMA(1,1,1) Model and plotting its graph\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mmodel3\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mARIMA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mts_log\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0mmodel3_fit\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel3\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisp\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel3_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msummary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mresiduals\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mDataFrame\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel3_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mresid\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'ARIMA' is not defined"
     ]
    }
   ],
   "source": [
    "#Analysing ARIMA(1,1,1) Model and plotting its graph \n",
    "model3 = ARIMA(ts_log, order=(1,1,1))\n",
    "model3_fit = model3.fit(disp=0)\n",
    "print(model3_fit.summary())\n",
    "residuals = pd.DataFrame(model3_fit.resid)\n",
    "model3_sse = sum((residuals**2).values)\n",
    "model3_aic = model3_fit.aic\n",
    "plt.plot(diff(ts_log))\n",
    "plt.plot(model3_fit.fittedvalues, color = 'blue')\n",
    "plt.title('RSS: %.4f'% model3_sse )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 829
    },
    "id": "RKlLicongVj9",
    "outputId": "218891c0-4e32-4a3d-d884-18796f2940cc"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ARIMA' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_59981/3955817280.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m#Analysing ARIMA(2,1,2) Model and plotting its graph\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mmodel4\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mARIMA\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mts_log\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;36m2\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0mmodel4_fit\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mmodel4\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfit\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisp\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel4_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msummary\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mresiduals\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mDataFrame\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel4_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mresid\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'ARIMA' is not defined"
     ]
    }
   ],
   "source": [
    "#Analysing ARIMA(2,1,2) Model and plotting its graph \n",
    "model4 = ARIMA(ts_log, order=(2,1,2))\n",
    "model4_fit = model4.fit(disp=0)\n",
    "print(model4_fit.summary())\n",
    "residuals = pd.DataFrame(model4_fit.resid)\n",
    "model4_sse = sum((residuals**2).values)\n",
    "model4_aic = model4_fit.aic\n",
    "plt.plot(diff(ts_log))\n",
    "plt.plot(model4_fit.fittedvalues, color = 'blue')\n",
    "plt.title('RSS: %.4f'% model4_sse )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "id": "kuVOm8M_gVfi",
    "outputId": "f8ed3c4b-f4f7-416a-ba6c-69a2c0a7c5a1"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model1_aic' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_59981/4094194527.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m#Analysing AIC and SSE of all 4 models\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      2\u001B[0m results_df = pd.DataFrame({\n\u001B[0;32m----> 3\u001B[0;31m     \u001B[0;34m'Arima(0,1,1)'\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mmodel1_aic\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"{:.3f}\"\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel1_sse\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      4\u001B[0m     \u001B[0;34m'Arima(1,1,0)'\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mmodel2_aic\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"{:.3f}\"\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel2_sse\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m     \u001B[0;34m'Arima(1,1,1)'\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mmodel3_aic\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m\"{:.3f}\"\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel3_sse\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'model1_aic' is not defined"
     ]
    }
   ],
   "source": [
    "#Analysing AIC and SSE of all 4 models\n",
    "results_df = pd.DataFrame({\n",
    "    'Arima(0,1,1)': [model1_aic, \"{:.3f}\".format(model1_sse[0])], \n",
    "    'Arima(1,1,0)': [model2_aic, \"{:.3f}\".format(model2_sse[0])],\n",
    "    'Arima(1,1,1)': [model3_aic, \"{:.3f}\".format(model3_sse[0])],\n",
    "    'Arima(2,1,2)': [model4_aic, \"{:.3f}\".format(model4_sse[0])]\n",
    "}, index=['AIC', 'SSE'])\n",
    "\n",
    "results_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LwN_Of95rKuf"
   },
   "source": [
    "### Predictions by my best model which is ARIMA(2,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7aeEUuBqm2Ih",
    "outputId": "822513be-5869-4e1a-8ac0-36220fe0910c"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model4_fit' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_59981/439102782.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[0;32m----> 1\u001B[0;31m \u001B[0mpredictions_diff_log\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpd\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mSeries\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mmodel4_fit\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfittedvalues\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcopy\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      2\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"The predicted log residuals are like this\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mpredictions_diff_log\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mhead\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"Predictions similar to what we had before taking residuals of log\"\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0mpredictions_diff_log_cumsum\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mpredictions_diff_log\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcumsum\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mNameError\u001B[0m: name 'model4_fit' is not defined"
     ]
    }
   ],
   "source": [
    "predictions_diff_log = pd.Series(model4_fit.fittedvalues, copy=True)\n",
    "print(\"The predicted log residuals are like this\")\n",
    "print(predictions_diff_log.head())\n",
    "print(\"Predictions similar to what we had before taking residuals of log\")\n",
    "predictions_diff_log_cumsum = predictions_diff_log.cumsum()\n",
    "predictions_diff_log_cumsum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "t7BSDepqsfTm",
    "outputId": "e88d754f-6295-41f0-e516-15a1ecabd45b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01    4.718499\n",
       "1949-02-01    4.718499\n",
       "1949-03-01    4.718499\n",
       "1949-04-01    4.718499\n",
       "1949-05-01    4.718499\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The first value which was subtracted in log operation\n",
    "pd.Series(ts_log.iloc[0], index=ts_log.index).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2J4cDa5Um2Z3",
    "outputId": "74586e53-fcfd-4dd8-a933-2c6debc25614"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The predictions just after the log operation took place resemble values like these\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1949-01-01    4.718499\n",
       "1949-02-01    4.728079\n",
       "1949-03-01    4.745570\n",
       "1949-04-01    4.773241\n",
       "1949-05-01    4.768720\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The predictions just after the log operation took place resemble values like these\n",
    "predictions_log = pd.Series(ts_log.iloc[0], index=ts_log.index)\n",
    "predictions_log = predictions_log.add(predictions_diff_log_cumsum,fill_value=0)\n",
    "print('The predictions just after the log operation took place resemble values like these')\n",
    "predictions_log.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "4PDODoVZtuHl",
    "outputId": "369e42d5-3894-4284-d477-3596c705769f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RMSE: 90.1046')"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sMeh6mSkWQgghhBBnKV3vvEZxv8hoSZ8QQgghhBCu00cPo0uKJnoYrhuuRnEfZY2R9AkhhBBCCOEa+6dbsP/6Qez/+9xED8Ul2m4zZoCd1Sh2sEZDSxO6u2t8BjYOJCgWQgghhPAC+yfvo5//DWgNkyX/9mSD0ZhjpPQJq6NW8dkzWyxBsRBCCCGEh9m3vINe9xTMmI269AtGW2TbJGiLPFKN4j7KERSfRSkUEhQLIYQQQniQ/f1/of/6e5i9ENPdP4GEJLDbjVlYH9dfo3iEnGKsRq1ifRYttpOgWAghhBDCQ/TJevT/PgdzFmO640co/wCUtS/AnAwBZL2LQXFkFCg1edJCXCBBsRBCCCGEp1SUgdaYLr0K5e9vbItytEWeBAFkXTVEWE+N3Qnl5w9hkZMj0HeRBMVCCCGEEB6iayqMH+IST2105N86ZmF9mK6vGXmW2MEajZacYiGEEEIIMUh1JfgHQERU/yYVOAVCQifHrGpdNWqkyhMO1rOrgYcExUIIIYQQHqJrKiA2AWU6I8SyxqB9vHyZttmMahIjVJ5wUNYYaKhFa+3lkY0PCYqFEEIIITylpmJg6oSDNcb30yfqq40qGSM17nCwxkB3N7S1eHdc40SCYiGEEEIID9A2G9RWo2IHB8UqKhbqfXxWtbwEAJWc5tLhqq8s29mSQiFBsRBCCCGEJzTUgq0XYhMG77NGQ1cHdLSN/7hcpMuPG2XWElNdO6G/q50ExUIIIYQQwqHaqDyhhkifUJMggNQnjkNMgrEw0BVWR6k5386VdpUExUIIIYQQHtBfjm2I9IlTZdl8Nyim/Di4mDoBQGg4+Pn7dKDvDgmKhRBCCCE8oaYSAoMgPHLwvr7av77awEN3dkBtFSolzeVzlFJ9ZdlkplgIIYQQQvTR1RUQG28Ei2cKDQc/P9+dKa4oBa1dXmTXzxrjs4G+uyQoFkIIIYTwhJqKIStPAEbd4kjfbXahy48ZPySluXWeipSZYiGEEEII0Uf39kJd9dA1ih2iYn13VrX8OEwJcr3Fs0NUDJxsMMrRTXISFAshhBBCjFV9jdH4wslMMfRVoPDR9AldfhySpg7uxDcSawxoO5xs8Mq4xpMExUIIIYQQY1XjKMc2RI1iB2sMNDUYs8o+RGsN5SXu5xNzWqm5umrPDmoC+Lly0F133cWUKVMwmUyYzWbWrFlDa2srTzzxBLW1tcTExHDfffcREhKC1pp169axZ88eAgMDufPOO0lPT/f26xBCCCHEWULbbei//BZSMzAt//xED8clunqYcmwO1mjQGhrrICZ+fAbmioY6o6lI8jT3z+2rVqGPH0Vlz/LsuMaZyzPFq1ev5vHHH2fNmjUAvPbaa8yePZu1a9cye/ZsXnvtNQD27NlDVVUVa9eu5bbbbuP555/3zsiFEEIIcVbSr/wV/fH76F2fTPRQXFdTAUEWo8qEE8qRr+trC9PKjwOut3c+nQqLhNhEdOFhz45pAow6fWLHjh0sXboUgKVLl7Jjxw4Adu7cySWXXIJSiqysLNra2mhsbPTMaIUQQghxVrNv34x+9xWjfFmjjwWPw9DVlRCbOHQ5Nof+DnC+lVd8qvLE1FGdrzJzoPCwkYYxibmUPgHw6KOPAnDZZZexYsUKmpqaiIw0ilNHRETQ1NQEQENDA9HR0f3nRUVF0dDQ0H+sw4YNG9iwYQMAa9asGXCOGB0/Pz95H32QPBffJM/FN8lz8U3j9Vx6ivJo+MvT+M+ci39GNu1vv0JUVNTwgaaPqKuvxi9zJhHDvE86NJQawNLZSogH3k9PPZeTtZX0xiUSnZI6qvM75p5P88fvE9nVht8oZpt9hUtB8cMPP4zVaqWpqYlHHnmExMSB+TJKKbf/wK5YsYIVK1b0/76ubvJ8GvRV0dHR8j76IHkuvkmei2+S5+KbxuO56OZG7I8+ACHh2G6+n94dH0FvD3XHilBhEV6991jp3h7sNVXYF1088vsUGk57WQmdHng/PfVcbEX5kJAy6mvp+BQAGnZ8gmlKyJjH421nxrEOLqVPWK1WAMLDw1m0aBGFhYWEh4f3p0U0NjYSFhbWf+zpb2p9fX3/+UIIIYQQZ9I2G/Y//BLamjHd9SAqLAJl7ZsBnQwpFLXVRlmy4RbZOfhYBzjd3QXVFajRLLJziEs0cqmPTu684hGD4s7OTjo6Ovp/3r9/P6mpqSxcuJDNmzcDsHnzZhYtWgTAwoUL2bJlC1prCgoKsFgsg1InhBBCCCH6HSuAwsOor9yCSs0wtjmCYl9blDYURzm22GHKsTlExfjWa6osA20f1SI7B6UUZORM+sV2I6ZPNDU18etf/xoAm83GRRddxNy5c8nIyOCJJ55g48aN/SXZAObNm8fu3bu55557CAgI4M477/TuKxBCCCHEpKZLigBQsxed2hhpBMW6oQ5fzyjuL8c2XDe7Psoaiz64G621T+RK677KE4wxF1hl5qD3bkM3NaLCJ+dk6IhBcVxcHI8//vig7aGhoTz00EODtiuluOWWWzwzOiGEEEKc/UqLICwCIk5LtwwN76tA4TupBk7VVIAlBBUSNvKxUdHQ3QVtLeDK8d5WfhwCAiEmbkyXUdNnogEKj8CCCz0xsnEnHe2EEEIIMaF0aRGkZgyYOVVKGbPFjfUTODLX6JpKl2aJ4bQOcD6SV3yqvbN5bBdKTYeAgEmdQiFBsRBCCCEmjO7phorSU7nEp4uMRvtS/q0z1RUoF4NiR61i6ic+KDbaOx8bUz6xg/Lzh2nZ6Em82E6CYiGEEEJMnPLjYLejpg4OipU12uerT+iebmOMrlSeAOjraucTFSiaGqC1Zcz5xA5qeg6UFaM7OzxyvfEmQbEQQgghJoxjkR1DBMVERsPJerTdNr6DckdNFWgNrlSeACOP2D/AN9InxtDeeShq+kyw241qIpOQBMVCCCGEmDilRRAceiqt4HTWaLDZoPnk+I/LVVVlAKj4JJcOV0oZtYrra7w5qhHpri7s77wCZj+PzRSTng1KTdoUCgmKhRBCiElO2+3o2ir0/h3opsaJHo5bdGkxpKYPWZ5MRToaePjuYjtdXAB+/pA01fWTJrhWse7qwv67R6DgIOrGu1EWz3ShU5ZgSEqbtIvtXGrzLIQQQgjforu60P9cjy4phBOl0GXkcarFS1G3fneCR+ca3dsDJ46jVnxx6AMiT2vgMS1r/AbmBl2cD1MzjIVmLlLWGPSBnV4clXP9AXHeftS37sV0wWc9en2VmYP+5AO0zYYyj7GixTiTmWIhhBBiMjq4E/3Bm6AU6sLlqG/cCdNnosuKJ3pkrqsohd5eGKryBPR3tdM+WqtY9/ZCSSFqWrZ7J1pjoKnRWKQ3jrwdEAMwfabxAc3RFGQSkZliIYQQYhLSeQcgcAqm7/0C5Wf879zeWI9+8x/onm6Uf8AEj3Bk/Z3spqYPfUBwKAQE+FZb5NOdOA493UYurTvik43/VpQNvcDQS/Q/17kVEPfY7Dy5tZK58cFcNj3CpXuo6TloQBceGbKiiC+TmWIhhBBiEtJ5+yEztz8ghr4qAtpuBFuTQWkRBFkgOn7I3UYDj4nNvx2OLjaqLCg3g2LHhwBdWuTxMTmjtUbv2Q7zL3B5hnjd7ho+Kmnh2Z3VVLe6OKsdGW18mKkoHcNoJ4YExUIIIcQko082QFU5asbsgTuS0oz9k+Sra13S18nONEw4EhmFPumjC+2K8yHc2p/m4bLoeOPDwDgGxVSVw8l61Mx5Lh2+5XgzbxacZHl6GCaleHZHtdHsYwRKKUhIRldNkg9mp5GgWAghhJhkdP4BANSM8wbuiI030g0mQVCsbTYoP45KdZI60UdFRvvwTHE+pGcNWTljOMpkgtSMUzWax4E+tMe498y5Ix5b1tTF77ZXkhMTxF3nJ3D9edHsrGhje3mrS/dS8clQWT6m8U4ECYqFEEKIySb/AFiCIWXagM3KZIbEqejyYxM0MDdUlRv5uM4W2TlYo+FkgxFE+xDd2gw1FW6nTjio1HQoPz5ur0sf3guxiajouGGP6+y188sPTxBoNvH9ixLxMylWZUcyNSKQ53ZW09FjH/lmCcnQ0mS8R5OIBMVCCCHEJKPz9kPWLCMIPoNKToPyYy591T2RdEkhwMiLsazRRp50U8M4jMoNx0aXT9wvNcP4UFDp/TQD3dtj1CR2YZb4D9urKG/q5v7PJBJlMcrM+ZkUdyyKo669l78fHHnWXiWkGD9MstliCYqFEEKISUTX10BtFSp79tAHJE+D1hbw9SYepcUQOAXiEoc9TEX2dbrzsRQKXZwPJhNMnT6q8x0fBsZlsV1xPnR1jhgUFzd0sul4M9fOimJuQvCAfTmxFlZkhPN/RxooOdk1/P36qmvocQj4PUmCYiGEEGIS0XlO8on7KEfLXh/PK9YlRZAybcjZ7gEio4zjfayrnS7Oh6SpqMApo7tAXKLxoWAc8or1ob1GAO/sg1SfT0pbMCn4fHbkkPtvnBuDxd/Eczurh79hVKyR2y4zxUIIIYTwmvz9EBIGialD70822g3rE8fHb0xu0nY7lBWjRsonhlOVHXyogYe22+FYwehTJ+jL/06ZNi4zxfrIXpiWZbRhHsbWshZyYy2ETxm6jUXYFD+uzoniQHU7tW09Tq+jTCaIS5p0FSgkKBZCCCEmCa01Ou8AKnu20zJmKjjUqBXryzPF1RXQ1TnyIjuAoGAIDPKt9Imqcuhod79pxxlUagaUHUPbvbfYTre1wPHCEVMnSpu6KG/u5oKU0GGPW5wcAsDuirZhj1MJKTJTLIQQQggvqa2Exjo4sz7xmZLTfLpWsWN21Gknu9MopcAajW70naBYF+cDY1hk5zA1w/hwUF3pgVE5kbcftH3EoHhraQsAS1JChj0uJTyA2GA/dlaMUJ4tIRnqa9BdnYN26a5On6smAhIUCyGEEJOGztsPOM8ndlDJU6Gy3Kg64IuqykGZIC7ZteN9rVZxcT5YQiB2+EWCI3GkjzgqcXiDPrzXaBSSljXscVvLWsiJCeqvOOGMUooFiSHsq2yj2+a8PFt/BYqqE4PHtOF17Pd9Hd3ZPvILGEcSFAshhBCTRd4Bo4NaXNLwxyVPA1uvEXz6IF1ZBjHxKP/hAzAHFRllzJD7iP6mHcN14nNFQgr4B3its53W2mjakT17QDvwM1W2dHOssWvE1AmHhUkhdNk0h2o6nB8UbwTFQ1Wg0Hn7ISoGNcXi0v3GiwTFQgghxCSgtUbnH0DNmD1iBzVHBQqfTaGoOmF8ve4qazQ0n/SJmW/d2Q4VpahpY0ydAJTZbKS6lBZ7YGRDqK2E+hqXUydcDYpnx1kIMCt2nhgmhSIuwah4cUZese7ugsIjqBlzXLrXeJKgWAghhJgMKsug+eSIZbUAYybZz88nF9tpuw2qT6DiR5jtPl1kNGgNJ32ggcexo6D12POJ+6ipGVBaZFS08DB9eK9xj5zhg+JPylqYbp1CbIhrM/eBfiZmx1nYNUxesfLzh5iEwRUoivKgtweVM3wK0ESQoFgIIYSYBHTBIQDnTTtOo8xmSEz1zZniumro7TVSB1ykrL7TwMOxyI5pw+fouiw1w6hkUVflmeudRh/ea9QMHqZBSm1bD0frO7kg1bVZYocFiSFUtvRwornb+UEJyYNnio/sA7MZsnLdut94kKBYCCGEmAxOlBgLpmLiXTpcJaVBeYl3xzQalcbCKxXvZvoETHgFCq01es82o2lH8PBVGlx1arGdeykU2majd5iSZ7qnB/IOoGbOHTbdZluZkTpxoYupEw4Lk4yax8OlUKiEZKipQPf2nhpX3n5Iy/S5fGKQoFgIIYSYFHRFCSSmjphP3C85DZoa0C1NXh2Xu7Rj8Z87QXFfV7sJnykuOAglhajlqzx3zaRUMPu5tdhO22zYn32c+ru+gj5WMPRB+7ZDRxtq/oXDXuuT0hamRgSSGBbgzqiJCwkgJTxg2BQK4lPAZoNaYxZct7caNZNzfC+fGCQoFkIIcQ7RzSex/89v0EV5Ez0Ut2itjcVdzrrYDUGlTDN+8LUUisoyCItwa6ZVTbEYTTwmeKbY/s4rEBqOumC5x66p/PwhaarLZdm03Y7+89Ow+xMwm9EbXh96rB9vMHKxZw+JdZwAACAASURBVDoPQBs7ejlS2+H2LLHDgsQQDtW0094zdM3h/rJsjgoUBQeNmsk+uMgOJCgWQghxDtGfbkZv24T9Vz/E/vrffLKBwJBaTkJri/PWzkPx0QoUuqrcvVlihwlu4KFPlMDBXajln0f5uzerOhJjsV2x8eFnuDFojX7pOfTWjairrsNy5ZfRuz5GnzGDrhvr4dBe1AXLjXbSTmwvb0GD2/nEDguTgum1w74qJ/WGE4zFlI6ybPrIfggIGHMnQG+RoFgIIcQ5Qx/cDbEJqMVL0W+8hP2XP0DXVEz0sEZ2ohTAvZni0HAIj/SpmWKtNVSWG7mm7hqnBh7OAlP97qsQEIj67ErP3zQ1HdpaoKF2+LG9+lf0B2+iPnc16qqvYll1Ldg1etNbA4/butGYkf3M8DPauyvaiA32IzV8dEF+TowFi7+JXU7yitUUi/Hc+lJm9JF9MD3X5frU483loNhut/PAAw+wZs0aAGpqanjwwQe5++67eeKJJ+jtS6Lu6enhiSee4O677+bBBx+kpqbGOyMXQggh3KC7uiD/IOq8xZhuvg912/eh+gT2n9/bX7rKV+kKIygmaap7Jyb5WLvnliZobx3VTLGyRns9fUI31mO/7+vYX31hQHCsG+rQn25BXfw5VPDoZlWHo9IyjfsMk9Zj3/gv9Nsvoy65HHXNt1BKYY5LhLmL0R++a/z5pm82+eP3IXMmapiOeza75kB1O3MTgl3PUz+Dn0kxNyGYXRVtzme5E5LRleXokw1QWeaTpdgcXA6K33rrLZKSTtUUfOGFF1i1ahVPP/00wcHBbNy4EYCNGzcSHBzM008/zapVq3jxxRc9P2ohhBDCXQUHjfqos+YDYFp0MabVT0NQMPYP3pzgwY3gRAkEh0JYhFunqeQ0qCgdsPp/QvXNGLpVecIhKhZamozFWt5yrADaWtBv/R39pyf6m4Xo998wZl5XfME7901JhylBkH/Q6SH6gzeNQPdrtw8IYk0rvgCtLejtm4wNRUegpgL1mRXD3rKgvoP2HjtzE4LHNPSFicE0dPRyrLFryP0qIQWqytF5+4zf++giO3AxKK6vr2f37t1ceumlgPEp5NChQyxZsgSAZcuWsWPHDgB27tzJsmXLAFiyZAkHDx4cMUdGCCGE8DZ9aLeRz3hafVRljTb+J12U59P/r9IVpZDkRuUJh9R06O3xmXbP2lFCzI0axQ4qY4bxw9EjHhzRQI7cV7XyWiP3fO3P0Q216C3voBZ8BhUd55X7KrMZMnPRBQeGHtfJeqg6gZq7ZHCOcGYupKajN7x+apY4cApqwWeGvefeyjZMCs6LG1tQvCApBAV86qw0W3wydHWiP9kIlhBwLAD1Qc4bYZ9m/fr1fP3rX6ejw+hx3dLSgsViwWw2HozVaqWhwegy09DQQFSUUTrFbDZjsVhoaWkhLCxswDU3bNjAhg0bAFizZg3R0dGeeUXnMD8/P3kffZA8F98kz8U3efO51B3Zh3nWAiITBn6l3D53IS1bNxLZ24XfaHJdvUxrTW1lOVMuuYwwN9+b3jkLqAdCGmoImrtw1GPw1HNpaaqnPXAK0ZnZKJN7y5p06IXU+PkzpbyY0EuvHPNYhtLUWEt3VCwxt95HR+YMmn/7C/Tqu6Gzg8iv3IS/F//NaJu/hNY//5ZIk8bsaFbSp+PQLpqByCUXDxiDn58fMTExdFz9NZrXPkxo8WGad37MlM9cSnjy8B88DtadYEZcKNOSxhboRwO5CdXsrurkO58d/P50z5hFI8CRfQQuWUZErHc+WHjCiEHxrl27CA8PJz09nUOHDnnsxitWrGDFilNT+3V1E9+lZrKLjo6W99EHyXPxTfJcfJO3nouurcJeUYr9kssHXV/HGcFDw86tmC74rMfvPVa6oQ7d3kqnNZZuN98bHRAEAYG0HN5H23mLRz0GTz0XW3EBxCVR3zDKds1pmbTv20GXl/7u2o4XQVyi8VpnLcJ0z2rsf3gMcufRFB4FXvw3Q6ekA1C/dQum85cO2Gff8TFYQjgZEok6bQyO56JnzIWwCJrWPgKd7XQvuGjY59XabeNwVQvX5EZ55LnOi5vCX/fWkl9aSZRl4CI6bTmVg92dPsMn/t1NTBw613rEj2n5+fns3LmTu+66iyeffJKDBw+yfv162tvbsfWVsmloaMBqtQLGrHF9fT0ANpuN9vZ2QkM9n5QuhBBCuEof2g2AmrVg8M6EFKNTXJH3vpYfkwpH5Qk3F9mB8VV7yjR0mXvd0rym6sTo8on7qKxcKClEd3Z4cFAGbbdDVfmp2rqAmjkX02PPYbr9hx6/3yAp04xazPmDUyh0/gHInuV0dl35+6OWrYT2NohNgMyZw97qQFU7dg3zxphP7LA42ag5vWOoFIrQcCMfHnx6kR24EBRff/31PPPMM/zud7/j3nvvZdasWdxzzz3k5uaybds2ADZt2sTChcbXMgsWLGDTpk0AbNu2jdzc3FGvahRCCCE8QR/cbbRHjk0YtE+ZTJCe7bMNPXRFX6tmd2oUn0alphs1cO12D47KfbqrC+pr+mvXjobKmgV2O3jjWTXWQ1fnoMoYKiQMNSXI6Wmflrdww8tH+fGGUl7cV8ueyjanzSyGo0xmyMo1AuDT6LpqqKtGZQ8fUKqlV0CQBbX0ihHjrj2VbQT5mciKdv663JESFkB8iD+flg8OipVSxgfPiCiIG/2zHw+jrlP8ta99jX/961/cfffdtLa2sny5UQtv+fLltLa2cvfdd/Ovf/2Lr33tax4brBBCCOEu3dMDeftRufOdBgsqfQacKEF3OGlCMJEqSo0OcKFhIx87lJR06OyAuirPjstd1X2VJ0axyK5fRjaYTOgCz6Vz9nMsskt0fXwlJ7v4748rCQ4w09Fj5+VD9fx0YxnfePmo09q9w1HZs6GmckAzDkeQrGaMEBSHRWD65Z9QK7447HFaa/ZUtnFevAU/k2cmLZVSLEoOYX9VOx09gz98mf7zJky33O/zk6QuLbRzyM3NJTfXWLUbFxfHY489NuiYgIAA7r//fs+MTgghhBirwsPQ1dlfim0oavoMo/rEsXyYOW8cBzcyXVE26lliAJWagQZ0SfGwdWu9rb/yxFjSJ6ZYYOp0dIHz0mWjpav6WhG7GLQ3d/by6OZygvxNPLIihSiLP+09NgrqOvnDp1X8vwN1zE90rwawyp5lPKuCA6glffnteQeMFAQXgnUVZBnxmKrWHmraevjSTKvL43LF4qQQ3shrZG9VGxec0TZaTcvy6L28RTraCSGEOKvpQ7vBzw+yZzs/aFo2KIUu9K0UCm23Q0WpW53sBklMBbMflBV5bmCjUXUClAnGGJirrFw4XoDuHrou7qhVlkNIqNEJcAS9ds0vP6qgob2XH12S1L+4zOJvZm5CMF+YYeVofSf5dZ3ujSF5mlG2LM+YHdZao/P2o2ac57FZ1j2VbYDn8okdZsZaCA4wDZlCMVlIUCyEEOKspg/uhszcYfNCVZAFkqaii30rKKah1shzTRrDTLG/PySmoEsneLFdZRnExI25xa/KnAW9vUajDQ/SlWUQ79os8fM7qzlY3c5d58eTPURe7vL0cIIDTLye516VDWUyDcwrrqmEk/XDf6Bz097KNuJC/IkP8WyrZT+TYkFiCDtPtGKz+27N7+FIUCyEEOKspRvr4UQJKtd56oSDypgBxfkTviBtgBOOyhNjmCnGSKGgtHhCG5ToqvIxpU70y8wxZvU9nVdcWY5yoU71vwtP8vbRk3wpx8pn04eeVQ7yN3H59Ai2lrVQ09rj1jBU9myoq0bX16Dz9hvbRsgndlWvXbO/qp258aNv7TycxUkhNHfZKKj3fHWQ8SBBsRBCiLPWqVJsIwfFpM+Ajvb+BVe+QPeVYxtLTjFgdLZraYKTo6wPPEbaboPqCpeCzpEoSwgkp3k0r1i3NENr84j5xB09dv6yt5ZZsUF8Y27MsMeuzIoE4M2CRrfGomYYs8I6/4BRni0iasiqKaNRUNdBR6/d46kTDvMTgzErJm0KhQTFQgghzl7F+UaNVBeCSjXdaCOsfalecUUJREQZgeAYqFSjMQSlE5RXXFdjtJv2xEwxfaXZivPQve7NwjrlqDwxQtD+Zn4jzV02bpgXi3mEyg0xwf5cmBrKvwtPDlmRwanEqRASCnkHvJJPbFIwO37kBXmjERxgJjfOIkGxEEII4Wv08aMwdbprQUVMgrHK34cW2+mK0rHPEoOxgEupicsrruorx+axoDgXurvheKFHrudK5Ym2bhuvHqlnYWLwkHnEQ/nCDCttPXY2Fje5PBYjr3gWetfHxuz+DM/mE2dGTSEkwOyxa55pcVII5c3dVDR3e+0e3iJBsRBCiBH5VJ6ti3R3l1G5IW26S8crpSBjBro437sDc5G224w81zEssnNQU4IgLnHCguL+cmweSJ8AIHOWcd2jHsorriiDgECIjHZ6yBt5jbR227l+zvBpE6fLjg4iO3oKb+Q3YHcjn1tlz4a+6hqeyidu7bJR2NDJXC+lTjg4utt9XNrs1ft4gwTFQgghhmXf+gH2738TXV870UNxT/lxsNlQU10LiqFvsV31CSPHdKLVVkNPt2dminEstvN8+oTWGl1aNPwHpxMlEBqOCg51fowbVGgYJKZ6LK9YVxqLAJ21UW7psvF/eQ0sSQkhwzrFrWt/YYaVypaeoVsgO6Ec1Sai41BRsW7dz5n91W1Ga+d47wbFcSEBzIm38GbBSXpsk+vDtATFQgghhrd/BzSfxP6Xpye0eoG7dEnfV+suzhQDqIwc4wdfKM3Wt8hOJU31zPVS06GhFt3q4YC/8Aj2h+9Dv/bCkLv1saPoT7cMWwFEa83uilZaulxvj6yycqHwCNrm+jm6pQn7JxsHB/BVZcPmE792pIGOHjvXzXY+k+zMBSmhRFv8eKfgpOsnJaaCNQY1e4Hb93Nmb2U7Fn8TmR5q7TycL82MorGjl83HfeDDpRskKBZCCDEsXZQHIWFweC/6w3cnejiuKyk0coSH+Up8kKkZYDb7xGI7faLE+GEsbZFPo1Ici+08m0Kh842yYfrtl7Hv+HDgvo527M89DuGRqK/e6vQaW44387MPyvn2/xXxvwfqaO9xIdDNmmW0ry5z/fXol55Hr3sS/fGGU9s6O6Chzun73NTZy7/yG/jM1FDSIt2bJQYwmxQXpoayv7qdzl7XZk6VUpj+6wnUNd9y+35DMVo7tzI7znOtnYczN97CtMhAXj3sXtrIRJOgWAghhFO6oRYa61CrroUZ56H/vg5dXzPRw3KJPl4IaZnutdkNCITUDOODgBfpilJsD9yE/b1Xh0w70HabEZhHxQ7bdMQtfRUotBtBpCt04REjoJyeg17/VH/estYa/cLvob4G063fRQUPXUGjs9fOn/fUMi0ykFlxFv62v47b/q+YVw7X0zVMEOmY1deFrn2A0VXl6B0fgp8/+uV16Oa+UmmORYBOZopfOdxAt02PapbYYUFiCL12zYGqdpfPUaFhxp9HD6hs6aGmrddrpdjOpJTi6hwr5c3d7DrRNi739AQJioUQQjili4xFZypjJqYb7wbA/mffT6PQXV1QUeZWPrGDypgBx4+ie3u9MDKDPrwXGuvQ/1iH/cnV6Ia6U/vKjmH/xffh4G7Ugs947J4qJAysMVDiubxibbdBUR4qexamO34IwWHYf/couqUJ/cn7RtrEF65HTZ/p9BqvHK6nvqOXby+M48Glyfz6iqlMt07hz3tqeXJrpfPXY42GqFh04WHXxvrmP8DfH9N9P4fuLvT//o+xvX8R4ODc7caOXt4qaOSStDCSw0cfoObGBhFoVuyqmJhSZY7Wzt5eZHe6i6aGEW3x45XD9eN2z7GSoFgIIYRzxXkQEAAp01DRcahrvglH9qG3+HgaRVkxaDuuVp44ncqYYZT7Kj/m+XE5lB0zFp7d8B0oysP+s3uwb9+M/Z9/xv7IfdBQi7rt+8b77UmpGZ6dKS4vMVIYMnJQYZGY7vwRtDRhX/tz9N/+CDPOQ135Zaen17T28OrhBi6ZGkZOrFE7NzMqiJ8uT+E/ZlrZWtpCdavz0l5qeg4U5o34IU1XV6C3b0YtW4nKykVdeS360y3og7uMGsVmM8TEDzrv5UP19No1Xx3DLDGAv9nEefEWdle2TcgHyr1VbcSH+JMQGjBu9/QzKb6YY+VwbQf5dZOjw50ExUIIIZzSRXlGnV8/PwDU0isgZw76H76dRtG/yG5qhvsnpzuaeHgvhUKXH4OUdEwXfw7TQ08Z5dKe/2/0O/9EXbAc08O/x7ToYo+34lWp6VBdYeTReoBjllZlGjPBKi3TCPSPH4WAQEw334cyOa+Ju36P8WfohnmDy5ytzIpEKXjn6DAL1KbnQFMD1FUPP863/gH+fqjLrzbGeeU1EJ+M/YU/oEuKIDax/8+4Q117D+8cPcny9HCPBJPzE0Oobu2hosVDDUdc1N/aeRxniR0uy4ggOMDEq5NktliCYiGEEEPS3V1QWnyqIgN9C4Bu+A70dKG3vDeBoxvB8UIIt6Iiotw+VVmjwRoNXgqKdW+PUT85ZZpxv7hETA+sQV3/bUzfexTTN+/xWOmyM6m0TNDac9U1Co8YCxmtp4Ja05JlqFu/h+nenw37/h+qaefj0ha+PDOKmGD/Qftjgv05PzmEfxc1Oc0tVtNHzivWNZXobR+gLrkSFWa0Xlb+/pi+cRfU18DhPUPWT/7HwXpA85VZY5sldliQaASlu8c5hSK/roPOXvuEBMVB/iauzIxkW1nrpGjmIUGxEEKIoZUUga0XlZE9YLOKjjM6bu3+2Gdzi3VJoVul2M6kMnK8V4Giqhx6e6EvKAZQfn6YPrvqVH1ab8nKBbOfkdM8Rlpr9NHDqOk5g2a0TYsvQQ0zS2+za57fWU20xY8vzbQ6PW5lViQtXTY+KnFS2isxFYKCjeDc2TjffhnMfqjLvzRgu8rKRV1yufFz/MDKE9Wt3WwoOsllGRHEhgwO2EcjLiSApLAAdlWM78KzvY7WznHeae08ks9nR2I2Kf6wo4rtZS20ulF2b7xJUCyEEGJI2jGb2JdOcDo1/0KoOmF0AvMxurMdqspHtciuX8YMaKgbsADOU3Spkaus+qpBjCcVOMWoEuFCUKx7utG7t2J7Zg0NP77TWLx4uoZaOFlvpDC4aWtZC8WNXdw4L5ZAP+ehyOw4CynhAbxZcHLID2DKZIaMbKeL7XRdNXrrRtQll6MiBgff6ss3wqwFqDmLBmz/+8F6FIprZrn/TcNw5icGc7C6fdiqGp62p7KNrKggr7Z2Hk5kkB/XzY4mr7aDX2w5wddfPsp9bx3jT7uqXS5RN14kKBZCCDEkXZQHMfGosIhB+9S8JaAUevcnEzCyEZQWg9aMZpGdg8ro+yDgjSYe5cfAPwBiEz1/bReonDlQdgzdPHSurj5Rgn39WuzfvRH7Hx6D/AP0HN6L3vzWwOP6ZmeHqyzhzEclLUROMXPR1OHTRJRSrMyKpKihk4L6zqGPyciBilJ02+C0BP32P0Ep1OX/MfS5lhDM/99qVPqpb0MqW7rZWNzE5ZkRRFs8M0vssCAxhB675kC166XZxqKly0Zhfee4lWJz5ppZUfzt2kx+sSKVr54XTXCAmY9KWwgwe79msjskKBZCCDGI1tootZUxeJYYMGbdMmb4ZFCsj49hkZ1D8jQICPDKYjtddgySpqLMEzNzp2bOM8ZxZN+gfdpuw/7Uz9A7P0bNPR/TvT/D9Ou/EDBnEfrtfxqz8A6Fh2FKECS713Gvq9fO7opWlqSEYnJhIeGyaWEE+Zl4q6Bx6NfTt8jvzA8wuq0VvW0j6oLlqEjXZ3xfOlCHn0lxTa5nZ4nhVGm28cor3l/VhmZ8S7E54282kRtn4auzo3lkRSrPfTHDpec/niQoFkIIMVhdNTSfNNIInFDzL4Ty4+iainEcmAtKCsEa3b+oajSUnx+kZXk8KNZaQ9mx/kV2E2JqOlhCYKgUivyDRrOWG+/GdNO9qNx5KLOZkOu/Da3N6A1v9B+qjx6GjBnDVpcYyu7KNrpsmiUpri0mtPibWZ4exkclLZzsHKJ2dFqW0YXw6MAUCr3tA+juRi270uWxVbZ0s+V4MyuzIokM8hv5BDcFmE3MjrOMW17xnso2gv1NZEa534nP28zj0FnPXRIUCyGEGMQRDKoh8okd1PwLjGN3bR2XMblKHy+EseQT91EZ2VBaZFTh8JTGOmhrgZTxzyd2UCYzKmcO+vDeQXm6evtmCAwalGPrnzUT5ixGv/eaMQPb3mpU0BhFPvG20hZCA0zMcmPh18qsSHrtmn8XDk75UIGOLoSnFttprdGb34FpWahU178xcJR/+2KO88V/YzU/MYSq1h6vV2PQWrO3so3Z8RafDEB9kQTFQgghBivOg8AgSHL+1biKioW0TJ9KodDtrVBTMbZFdn1URg7YbB7tAEfZcePaEzlTDDBzrrFIrq/FMZxaWKfmLxmyvbDpi1+Djjb0e69BUb6Rt+1mPnGPTbPjRCuLkkPxcyNQSw4P5Lx4C+8cPYnNPsSCu4wcOHbUKHcHUHAIKstQS12fJe6x2dlY3MT5ySFYvTBL7OAozebt7nbHGruobe9lQeLQ7bXFYBIUCyGEGEQX5cG0zBHzXtX8C42WyPW14zSyEfQFsCotc+zX6m/i4bnSbP3d5NzMw/U0NXMuAPrQnlMbD+yEjjbU+cuGPidlGmrRxej3X0fv2Wp0gZuW5dZ9D1S30dZj54IU9wO1VVmR1LX38umJwcGkmp4DPd39z19vfhsswaiFF7l8/a1lrTR32bg8c/RpN66IDw0gMTSA3V5Oodha1oJJwfnJEhS7SoJiIYQQA+jODig/7nSR3enUgr4Uij2+MVs8pk52Z1ChYRCX5NG8Yl12DGITUFMmpmasg4qOg9jEAaXZ7Ns3Q1gEzDjP+XlXXQfd3egP34PUDKPEmxu2lrUwxc80qoVfi5JCiLH4Db3gztHEo+gIuvmkMeN94aVGaoWL3j3aSHyIP3Pivf9sFiQGc7DGu6XZPiltITfWQvgU7816n20kKBZCCDHQ8aNgt7sWFMcmQnIaetf4BMWDauWevq+3B523H6LjUCFhHrmfypgBRXmea1JSVjygacdEUjPnQsFB431rb4X9O1CLLh722wGVkIxassz4OcO9fGKbXbO9rJWFScEEmN0PP8wmxRWZkeyvaqesaeCfAxUeCTHx6KNH0B9vMJrOXHKFy9cub+riYE0Hl02PGJeKCIuTQ+i2aa+lUJQ1dVHe3M0FLi5mFAYJioUQQgzQPzOanj38gX3U/AuNwPFkgxdHZXzVb//Otdh+8T3s77+BbjJmDHV7K/Z3/on9R7fCoT2oued77qYZ2dDSBLWV7o21pQl9/OjAbZ3tUFuFmsBFdqdTM+dCVycU56N3b4XeXqepEwPOu+qrEBmNmufe+5xX20FTl21Mgdpl08PxM6khZ4vV9BwoOoLe8i5kz0YN0brZmXcLT2JWsCI9fNRjc0durIWwQDOflLZ45fpb+667ZBRpKucymVMXQggxgC48AvHJqGDXghc1/0L0639D79mG+uxK741rz1YICITeHvRLz6H/939g+gwoPQZdHZAzB9ON90DuPI/dU2XkoAFdlG/MirsyzsIj2J/5JTSfxHTPT1CzFhg7yo8b10z2jZlismeDyYQ+tNfoXhib4FJrbBUTj/lXf3L7dp+UteBvUsxPHH3N3PApflw8NZSNxc18Y24MFv/TZrWnz4StH0BLE+o/bnD5mt02Ox8UN7EkJZQILy6wO53ZpLggJZTNx5vo6rUP29VvNLaWtZAdHUSUh5uPnO1kplgIIUQ/bbPB0cOo7Fmun5SYAvFJRtDqRfrIPsiZg/mhpzD97LeolddARztqziJM//UE5vsfRs2aj/Lk198JKRBkARcW22mtsX/wFvZf/xgCAiAxBfuzv0ZXnTD2lxntnX0mfcISDNOy0Du2QP4B1PlLPfvenUZrzdayFuYlBg8MZEdhZVYknb12PihuHrC9vzxcaLjRcdFFn5S20NJt5/LMwZ0bvenC1FA6ezV7Kj274K6qpZvixi4uTJVZYndJUCyEEOKUkkJj1jXb+WKrMymlUOcthoJDxiI9L9B11VBTabQoBlRiKqarv4559VpMt34P5YGFdUNRJhOkZ4+42E73dKPXr0X/7RmYORfTj3+D6Tv/BWYz9t89iu5oh7JjEBIKbnRX8zY1cy7UVhnl1VxInRitwoZO6tt7PZLjmhUdRGbUFN4qaByY6x2fDHFJqBVfQPm5PkP67tGTJIT6M9uNusmeMCvOQqgXUii2lhnXk3xi9434PUF3dzerV6+mt7cXm83GkiVL+M///E9qamp48sknaWlpIT09nbvvvhs/Pz96enr47W9/S3FxMaGhodx7773ExsaOx2sRQggxRjr/AIB7M8WAmr0A/d6rkLcP5ro+S+fyuPpaEjuC4vGkMnLQb/w/dHsryjL07Jv997+Ag7tRn/8K6qrrjGA6OATT7T/A/pufYH/+v6GpEVLSvTYbOxpq5lz0Gy9BWiYqzrX0kNHYWtqCWRkVJDxhZVYkT22t5EB1O+fFG+kYymTC9PDv3brOp+UtHK7t4JvzYsa95bCfSXF+cggfl7TQbbOPavHhULaWtZBhDSQuJMAj1zuXjPgE/P39Wb16NY8//ji/+tWv2Lt3LwUFBbzwwgusWrWKp59+muDgYDZu3AjAxo0bCQ4O5umnn2bVqlW8+OKLXn8RQgjha3R7K7rg4EQPw206/wAkpKDC3PwqeXoOTAlCH9jlnYEd2QfhViOdYZypnDmgNXr/ziH36/oaIyC+6quYvvg1IyB2nJs9G/XVW2H/DigpnPimHWdKy4Kp01GXXuW1WzhSJ2b3zYx6wkVTQwkNNPPmGQvulFIuf+iowzJOMQAAIABJREFUbeth7dZK0iMD+Xy2d2sTO/OZ1FA6eu3s9VAKRV17D/l1nTJLPEojBsVKKaZMMeoQ2mw2bDYbSikOHTrEkiXGbMCyZcvYsWMHADt37mTZsmUALFmyhIMHD3qulI0QQkwS+tUXsD/+IPa//Bbd0zPRw3GJ7u2FwiOoGbPdPlf5+cPMuegDuzz+b76229F5+1E5cyZmljU9GyKsTjv36b3bAZw3vVi2EnXx54zf+FhQrPz8MP/XbzD1lVnzhtKmbipaeljiwUAtwGzicxnhfFreSmWL++2SbXbNbz6uoMcO378oCX8PzdK667z4YEICTHzsoRSKbY7UiVQJikfDpWWWdrudH/zgB1RVVXH55ZcTFxeHxWLB3FfL0Gq10tBglOJpaGggKsrIlzKbzVgsFlpaWggLG1gzcsOGDWzYsAGANWvWEB0d7bEXda7y8/OT99EHyXPxTd58Llpr6g7tQkVYsX/4HuaqciIe+AXmaN9OJevOO0BjVydhiz7DlFG8Nx0XfJbm3VuJaG/Gf5Q5vkM9l55jR2loaSJ08UUETdDfpeYLl9Ox4Q2iQoJRU4IG7Gs4uAt7yjSic53nYeu7H6Rj9nyCLvmc2w0vfMFY/r68XlSKAq6cM5XoYM99pf+NC0J5u7CJZ3fX89R/zHIr/eHZrSUcru3gocuzOC99Yv9eLp3eyObCesIirAS4WYXizOeyc3MlaVYLc9OTPD3Mc4JLQbHJZOLxxx+nra2NX//611RUVIz5xitWrGDFihX9v6+rqxvzNc910dHR8j76IHkuvsmbz0WfKMVeW436xl2YgkPpXfcUdfffiOnbP3A7V3c82bd/CEBL/FRaR/He6L7Wyo1bNmC6cnT1Xod6LvatmwBoTU6nbYL+LumZ8+Ctl6nb/B5qwWdObW9txn54L+qKa0b+8zTvQtpbWqHFOw0bvGksf1825lczIyYIOpqp8+A6TAV8a14Mv9texYtbC7kyy7UUiH1Vbfzl0zIuTQ9nQbRpwv99nh8bwJuHbWw8VMpCN3OuT38uJSe72HeiiWtnRU34a/J1iYlD58+79ZEkODiY3NxcCgoKaG9vx2azAcbssNVqBYxZ4/r6esBIt2hvbyc0VKbxhRDnDn3QyD1VsxagFlyI6cHHwRKC/Tf/5dGWwZ6m8w9A0lSjvfEoqIgoSE1HH9jh2XEd2WfkOU9k1YbMmRAaPqhzn96/w+j+52Yji3NFZUs3xxq7vJbjellGOHPiLazfU0tN68hpSg0dvTzxcQVJYQHctijOK2Ny15z4YIL9x5ZCobXmjzuqCPY38flsqwdHd24ZMShubm6mrc1IAO/u7mb//v0kJSWRm5vLtm3bANi0aRMLFy4EYMGCBWzatAmAbdu2kZub61MrbYUQwtv0gV2QnIayGl9rqsRUIzD2DzRa0Pog3dsDRUdQM1wvxTYUNWuh0d2uzTOzobqnBwoOTUjVidMpk/n/Z++8w6Oq0j/+OXcmmfTee6FD6EV6V7CyFuxlbWvDVZS1K4pd+WFZ26qrrr2jIE260nsnAVJI73363PP7YwQEEkiZJAPez/PkEefeOeedOVPeee97vl9E3yHInZuRtmM9rHLbeggOg8TTm178FTkiD9ZWzmpCCO4eEgXAWxsKT9nPfqDcxIxF2dTbVGaMiMHLxYYZLcVDJxgc58eGvFpsjpb146/KrmFPiYkb+kUQ4KLNjH9FTvuKqKys5Omnn+bBBx/kkUceoXfv3gwYMIBrr72W+fPnM23aNOrq6hg3bhwA48aNo66ujmnTpjF//nyuvfbaNn8QGhoaGu6CNNbDwb2ItAHH3S58/BB9BiG3rXMaZLgbWQfAakV0aV17h0gbAKqK3LvdNXFlpoPV0uFJMeBsm7CYYM82AKTFAnudttJa8adh1reDPFiknyc39Qtne5GRXw9VN3jOkoNVPLzkMIqAF89NJCnYvfq6RyQGUG9VWXO45vQnn4DR5uDjrSV0DvViQmr72FSfrZy2pzgxMZGXX375pNsjIyN54YUXTrrd09OT6dOnuyY6DQ0NjTONfTvA4XBWTE9ADBiO3Lga0ndCD9dZEbsCmb4ThIDW9jyndAFff9i1GQaNaPr8qgrb12MODESm9DiaZMp920FRoJXJukvomgY+fsitaxF9h8Cerc4fEs1wT/srcUQe7Lo+bb858rzOQfx+uJaPtpYQ4etBiLceX08FL73Cx9tKWHKwmr5RPjwwItYtK6n9on1JDfHik22lDInzx9uj6VXsL3eWUWV28NiYuHbXWj7baB+Tbw0NDY2/CHLXZvD2hdRuJx/s1R8M3sjNaxBulxTvhtgkhG/rej+FokP07IfcvQWpqsdp9jY4r5SwazPqj59BXhbVAF3TUK69ExEd5+wnTu7itCTuYIRej+gzGLljA9JuQ25fDz5+0Llnh8QjpcRsl81KoNqTDbnOFpr20MxVhGDakCj+uSCbp5bnnnT88p6hXNM7DJ3inkmjThHcPjCSh5bk8O3uMm7o1zRFjENl9cxPr+TcTkF0DvU+/R00TomWFGtoaGi4CCklcvcWRM9+CN3J1SjhaTjWQnHNHQi9e3wES5sNDu1HjJ7kmgHTBsDG1XD4EPyhSNHgvAf3oX7/MRzcB+FRiFsfwM9DR+0nb6M+fS9iwkWQdQBxwRWuicsFiAHDkOuWw97tyB2bEL0Htds6SinZU2Jif5mJjDITGeVmKk127h8WzZhk97tsvi63lrgAT+ICDe0yX5S/J29flExutZU6q4Nai4N6q0qnUC/6Rnf8j6rT0S3cm7HJAfy0v4IJqUHEBJy65URKyeyVh/D11HFd3/B2ivLsxj0+kTU0NDTOBnIznVa+J/QT/xkxcISzhSJjl/u0UGSmg82K6Np8046GED37I4VA7tqCaCQpljWVqLMfA19/xLV3IEZMROg98AkLo75TL+R3HyEX/+gczw36iY/Soy8YvFG/+xiMde3aOvHZjjK+2+NUd4rx96B3pA8FtVbe3lBEaogX8e2UfDaFGrOdPSVGLu/ZvoohoT4ehPp4tOucruTGfhGsz63jgy3FPDm2cfdGVUo+3V7KjoIa7h4S5ZYtIWciWlKsoaGh4SKOWByLXv0bP6lnP7droZDpu5z9xC5qAxD+gZDU2dlKctFVDc+5dwfY7Sh3P45IPj5xFgFBiJvvRw6fgNy/C1K7uyQuVyA8PJ3V/o2rwcPTuZ7twIFyEz/sLWdMUgC3Dow8apdcbrRx/4JsXv4tn1cmJbmNosKGvDpU2T6tE2cTwd56ruodykdbS9mcX9egbrHR5mDO2kI25tVxca9IbXOdC3GPd4+GhobGCUgpUVcvwvHk3cjCvI4Op0nIXZshsRMioHETAWcLxWBnC4Xd3o7RnYyUEllX49zMFp+C8HWdbJboPRCyDyCrKxs+Yd8OZz9uYkrjY3RNQ7nkmgZbUToS0X+Y8x89+7WLO53NIXlzfRFBXnpuG3QsIQZnZXT68Bhyq628t6m4zWNpKutya4nw9SA52H2q12cKF3QJIS7Akw+2FGNzqMcdK6q18tDiHDbn13HbwAj+Na6TtrnOhWiVYg0NDbdD1lSh/u/fsGOj8//XLkNcdmMHR3VqZF0NZGYgLph62nPFwOHIjasgfVe7VBplVQXk5yDzsyEv2/kjo6oCaqvB4UzMxXl/c+mcou8Q5E9fIHduQow89/h4pHRKtnXvjVDcK+FtEr0GQGInlFEu6sE+Dd/tKSOnysLjo+Pw8zz5+eob7csVvUL5Znc5vSK8GZ8a1C5xNUaVyc72wnou7haiSdW1AA+d4NaBkcxcnssDC3OI9PcgyEuHv6eOJQerkMBTY+PpG+2rPb8uRkuKNTQ03Aq5azPqR6+DyYi48hbkri3IrWuRl97g1l8Acs82kOpJ+sQNckSFYssaRBsnxerSn5Bff3jshsAQiIlHxCZAQNAff8GItJMl5FpFbBKERiC3b4ATkmKK8qGqHNGjr2vnbCeEwYDu8f9rl7myKs18u9vZNjEorvFK/lVpYewtNfHupmI6h3qTENRxFdrlmdU4JNpl/VbQL9qXG/uFs72wnuI6G+llJmrMDhKCDDwyKpZo/7bTff4royXFGhoaboO64hfkF+9BbCLK9FmIuCRUTwPy07chPxvikjs6xMbZuRn8AiDp9M5mzr7Uwcitba9CIdevgrgklKtuc1o4+7XMwrm5CCGc1eLVi5EW83FtBkeMPUT3MzMpbi/squSNdYX4G3TcMvDUlsQ6RfDA8Bju+yWLD7YU88z4hHaK8niklPx6qIoe4d7tpjpxtnJpj1Au7XFso6JDlSgCty4OnOloPcUaGhpugayvQ/74GfToi/LYbERcEgCi7zkgFOSWdR0b4CmQZiNy+3pEv3Oa3A4gBg2H+lrYv7Pt4qosh5yDiMGjEF3T2i0hPoLoMxhsVjjB3U7u2+6UYAuPatd4zjTm7qsgs9LCHYOapi4Q4q3nwm7B7Cgycrja0g4RnsyeEhMFtTYmdurYFo6zEZ0itIS4jdGSYg0NDbdALv4BzEaUK/6O8Dh2aVAEBEHnHsgtazowulMjN/3utCIePqHpd+rZH7y8kZt/a7u4dm4C/khOO4LOPcHHF7ljw7GY7HZI36VViU9DldnOt7vLOCfej6EJTVdwOK9TEB6KYP7+RjY4tjG/HqzC10NheDNi1tBwF7SkWENDo8OR1ZXIZfMQg0YhGmiREAOGQWGu26pQyLXLICoWmdylyfcRHp6IfkOdLRTWtqnqyR0bITwKohvXO21LhF6P6DUQuXMzUnU4b8zOALPpjO0nbi++21OO1SG5vpmmDIFeekYnB7Aiq5o6i6ONomuYOouDtbm1jEoKwOAm0nAaGs1Be9VqaGh0OPKXb8BuQ1xydYPHRb+hzvO2rm3PsJqELMrHdiiD59Nu5oqvM7ht7kEeXpLDK7/nsyDj1NU6MXQsmIzO5NXVcVnMsH+n03GtIy+59h3sVLk4lO6Ma+92pyZyN9cYhZyNlNbbWJhRxbiUQOICmt+Xe2HXYKwOyZJDVW0QXeOsyq7B6pCcq7VOaJyhaEmxhoZGhyLLipGrFzsdzSJiGjxHBIdCaje3TIrVNct4q9tUNtv8GZcSSI8IH/SKIKPMxHubitmYV9v4nbumQUgYct0K1we2b7vTpa6jWif+QPTsDzr90RYKuW+HU8vZV7u83hhf7yoDnIoSLSE52IteEd4sSK/EoUpXhtYoUkqWHKwiNcRASkjbazdraLQFWlKsoaHRoch5X4EQiAuuPOV5ov9QOJyJLC1qp8hOj1QdfJ5tZ3VkP67rE8bdQ6K5f1gMz05I4J2LU4kP9OT9zSVY7GqD9xeKghgyBvZsbdzkoqWx7dgE3j7QuYdLx20uwscXuvZCbt+INBkhM11rnTgF+TVWlmVWM7lzEOG+LbcrvrBbCKVGOxvz6lwYXeMcrDCTXWVhYgdrJGtotAYtKdbQ0OgwZGEuct0KxNjzESGnrooda6FwHxWKxb/t5vuoYZwbZObynqHHHdMrgn8MiqSk3sZ3e8obHUMMHQuq6rQNdhFSVZ2mGb0GIPQtT6xcheg7BIrzkasXgaoiuvfp6JDcli93luKpEye9nprL4Fg/Inz1zEuvcFFkp+bXg9UYdIJRSe2rcKKh4Uq0pFhD4yxDmoyo61cc29jkpkhVRf32I/A0ICZfftrzRXgUJKS6TQvF5vw63sv1oH/VAf4xoUeDfbtpkb6MTgrgh70VFNZaGxxHRMdDYifkuuUtikPu34lM3338jTkHoaYKeg9q0Ziu5kgLh5z/NXgaILV7B0fknmRWmPktp5aLuoYQ5N067WqdIji/SzB7SkxkVphdFGHDmO0qq7NrGJ4YgG8DjnsaGmcKWlKsoXEWIS0W1DefQX44B7nZfSXM4I/Ndbs2I6Zci/BvmvOV6D8UMtORFWVtHN2pKTfaeOX3fJLrCngwuBi9oXF3qZv6R+ChCP6zqRgpG+7vFEPHQW4WMi+7WXHIfTtQX3sKdfZjqPO+QqrONg25fSMoStPc9doBERIOCSlgNkGXngiPjq9euyOf7yjF11NhSo8Ql4w3MTUIg04wP71t5dlWZ9dgsqucqznYaZzhaEmxhsZZgrTbUd97CQ7uc+rfbmo7/dsG57dYkPk5yJrK01ap5bb1yJ+/QAwdixh/UZPnEAOG/XH/jm2h+GJnGXa7yoN7PsVnxNhTnhvireeaPmFsLaxnfSP9nWLwSNDpkOubvuFO5mWjvvMCRMUhhoxB/vwF6tvPI431yJ0boVN3t9rMJvoMcf5Xa51okH0lRjYX1HNpj1D8XFRt9TPoGJcSyOrsGqpMdpeMeSJSShZmVJIYZKBbuHebzKGh0V5oSbGGxlmAVB3Ij15zVl6vvRMx4lzYvQVpbJ9NNgDyi3dRZ05DfeBG1DsuxTH9ehzPTv+jlePYRjOZfxj1wzmQ1Blx/d3NkgsTUXEQn4z8fWmjVVdXILeuRf3PK8jykpOOZVeaWZ5ZzeSqnUSGBSESUk873gVdgkkMMvDh5mLMDWy6E/6B0GsAcv2qJrW9yMpy1DeeAYMXyr1PIm6+D3H17bB7C+qs+yAvu8NVJ05EnDMGElIQ/Ye167y1Fgdb8uuoMrdNUugKpJR8uqOUYC8dF3YNdunYF3ULwa5KFhxom2pxRrmZzEoLkzsHaW5rGmc8WlKsoXGGI6VEfvkf5MbViEtvQBk9yVl5tNuR2zacfgBXxbB7C3RNQ1zzD8QFU50b4xwO5IdzUJ+9H7l3G7K+FvWtZ8HLC+WuR49zrmsqYsz5kJflrIi3EeqqRchNv6E+/U/UDauOO/bJuhy87RYu3/0DYuz5TRpP98emu1KjnSUHG9aOVYaOg+oK2Hdq22dpNjoTYlM9yr1PIULCEUKgjLsQZfqz8IcRiOjtZklxRDS6J15DhEW225xbC+qYNj+TZ1bmceP3B7lnfibvbixiXW4tahv+qGou24uM7CkxcUWvMLxcbHoRG+DJoDg/FmZUNaqC0hoWHajES68wOlnbYKdx5tO6Tn4NDY0OR879DLlyIeK8S1GObFhL6gxhkchNq2H4+LYPoigPaqoQU65DGXnusdhUFbnpN+SPn6LOeQr8A53J3IPPO7WHW4AYMhr5/cfIFb8g2kBuTKoqZGVAn8FQX4v8YDbqzk2IK/7OjsUr2WrvyQ1FvxN46z+h/9Amj9szwoeuYd4szKjioq7BJ1fVeg9yWiKvW47o2a+R2Byo774EBTko055ExB/v/ie69ER54jXIz0FExTb7sZ8tWOwqn2wv5Zf0ShICPblzcBR5NVZ2FxtZkVXDwgNVXNsnjKm9WqYD7EqklHy6vZQIX482M72Y0i2ER/MOsyKrmkmdXVeJrrE4+D2nlvEpgfh4aBvsNM58tEqxhsYZjLr4R+SCbxEjz0VcduPR24UQiEEjYd8OZG11m8ch9zurm6Jb7+NuF4qCMmQ0yqx3EFNvcSpNXH83IrVbi+cSBi/E8AnIrWuRVY1LnbWY4nwwGRH9hjqT9ynXIbeswf6vW/i4MohwaeLCO29ADBjW7MvF53cJoqDWys5i40nHhIcHYvBo5Ja1ja/Zri2wZxviqtsQvfo3eIoICmk0qf4rkFVpZvrCbH5Jr+SirsHMnpzEkHh/LusZylPj4vniis6MSgrgix1lbC+s7+hwWZdby6EKM1f3DsND1zbtBz0ivOkU4sVP+ypdWiFfnlmF1SGZ1FnTJtY4O9CSYg2NMxT1tyXI7z5CDByBuO7OkxI0MXikU/92S9tLmMn9OyEkHBq5NC48PFAmXoLuxQ9QhrW+ci3GnO98bKsWt3qsE5GZTjtikdIVodOhXDAV5aGX+X3IVLL8Y7l+eAoG/5ZtYBuW4E+AQcfCRuyfxbgLwG5Drm74canLf4GgUMTI81o0/9nOvlIjjyw5TL1NZea4eG4dGImn7vivOZ0iuHtIFHGBnsxeU0CZ0dZB0YJDlXy+o4y4AE9Gt6G+rxCCS7qHUFBrZXO+a/YZqFKy6EAV3cO9SQrWHOw0zg60pFhD4wxEblmD/PRt6NUfccv9CKWBS5exSRAd72yhaMtYVBXSdyO69W63jTYiItq5MW31IqTdxUlNZjr4+ELkMctpW0IqX4QOJjXEwMhWJC+eOoUJqYFsyKtrMBkT0fHQqz9yxYKTHpcsyoO92xCjJyH0WufbiewrNfL08jyCvXXMnpRIv2jfRs/10is8PDIWq0Py8m/52Bwd01+8KruGvBor1/UJR6e07XtnWII/YT56ftrnGjOPnUVGCmttWpVY46xCS4o1NM4w5N5tqO/PhtSuKHc83KhjmRDCWS0+sLdtdX3zsqG+Fk5onWhrlHEXQE2VyyvhMjMdkrsglGMfj0sOVlFSb+fGfhEorUz8J3UOQkoa33A3/iKorjhJZ1quWAB6PWLUuQ3e76/M/lLT0YT42QkJhPqcXgc5LtDAvedEkV5m5uNtJ6uMtDU2h8qXO0tJDfHinHi/Np9Prwgu6hbM7hITB8tbb+ax8EAlAQYdwxPcR/ZPQ6O1aEmxhsYZhFRV1E/+DZExKNOeQBhOfdlSDBwJUiK3tJ2Rh0zf5Zyra1qbzdEgPfpBRDRyxS8uG1KajZB/GJHc9ehtVofK93sq6BnhTZ+oxquPTSXSz5MBMb4sOVCFXW2gQtmjH0TFIZf+fFR2TpqNyLXLnK0yAa6V7DrT2V9qYubyXIKakRAfYXhiABd1C2Z+eiVrDte0YZQnsyKrhpJ6O9f1CWu3KywTU4Pw1iutrhZnVpjZmFfHhNRAPHRaGqFx9qC9mjU0ziSyD0BFKeK8SxE+p68uiahYpzVyGxp5yP07ISIGEdK+O/mFojgl0Q7tR+YcdM2g2QdBqoiUY0nx0kPVVJjsXJXmusc3uUswlWYHG3JrTzomFMVpaJJzEA45ZefkupVgNiHGXuCyGM4GMivMPL2iZQnxEW7qF0FqiIEPt5S0iWRZQ6hSMndfBakhhlO2ebgaX08d53YK5PfDNeRUWVo0RpXJznOr8gj21nNJd9c472louAtaUqyhcQYht64Fna5Zxgxi8EjIykCWFrk+HocDMnafpDrRXohh48HTgFzummrxkU12JHcGnJe4v9tTTvdwb9IifVwyB0C/aF8i/TxYcKDhFgoxdCz4+KH+US2WK36BxE6Q3MVlMZzpFNVaeWZFLt4eCrPGJxDWgoQYnG0FN/ePpNxoZ97+trVDPsKm/Drya6xM6R7a7oYXl/YIJcCg4+Xf8jHZmvcjwOZQeWF1PjUWB4+PjiPIS+tt1zi70JJiDY0zBCklcus66NYb4dv0HkQxcKTz/muXuz6ow4fAbIJu7dw68QfCx8+pW7z5d6TZ1OrxZGY6RMYi/Jyb6ZZlVlNutHNlmmsvcesUwaROQewuNnK4+uSKnTB4IUadB1vXI9cshcJcxLgL2j2BcqiSTXl1PL8qj/sWZLlMuaC1VJntzFyRi02VzBwXT7hvyxLiI/SK9GFQrB/f7Smnuh2c7+burSDCV98h/bhB3noeGB5DQa2VdzcVNdkZUkrJOxuL2V9m4r6h0aSEaIoTGmcfp02Ky8rKePrpp7n//vuZPn06CxYsAKCuro5Zs2Zx7733MmvWLOrqnB+WUkr++9//Mm3aNB588EEyMzPb9hFoaPxVyM2C0qJm2+SK0HDoPxS55EdkealLQzqqT9y1l0vHbQ5i2DiwWpw/GFqBlBIy0xEpzmqszSH5bnc5XcO86BvluirxESakBuKhiMbl2caeDwLkZ++AX4BTd7qdqDTZ+XxHKbfNPcSzq/JILzNhsUtmrczj9XUF1FlOb0XdVhhtDp5ZkUe50c4TY+JJCDS4ZNwb+4Vjcah8vasNN6UC6WUm9paauLhbSJsrTjRG7yhfrkwLY2VWDUsPNU3H/OttBSzLrObKtFCGJ2rudRpnJ6dNinU6Hddffz1z5szhueeeY/HixeTl5TF37lzS0tJ44403SEtLY+7cuQBs27aNoqIi3njjDW6//XY++OCDNn8QGhp/BeTWtSAURL9zmn1fZeotgET9xrXvR7l/J8Qmduzmr9TuEB6FXL/ilKfJ8hLU339Fff9VHA/eRPWbzx5fJSsrhtpq+KOfeEVWNaVGO1f2apuNUAFeekYk+rM8s5o668lJpggJRwwYDg6705ylBZbYLaGkzsaMRdl8t6ecpGADD4+K5cO/deKNC5K4omcoK7NquOeXLDbmndwP3dbYHJKXVueTVWnmXyNi6Rbu7bKx4wMNTEwNYtGBKvJrrC4b90R+3FuOn6fChNSOlTK7omcofaJ8+M/mYrIrG1ejsDlUft5fwVu/ZzE03t+lvfUaGu7GaZPi4OBgUlJSAPD29iY2NpaKigo2bdrE6NGjARg9ejSbNm0CYPPmzYwaNQohBF26dKG+vp7Kyvbp09LQaCrSbkfWVB3/Z2u7L0JXILeugy49Ef6Bzb6vCI1AXHAlbF2H3L3FNfHYbXBwb/urTpyAEAJxzhjYvxNZcXIlXFZX4nj6XtSHb0V+8qYzkQ+Pwrx8AXLdsURaZmU4x0vpil2VfLu7nE4hXvSPabuNUBd1C8FslyxrpFonzr/cKQ/XThvsyow2Hl92GKNd5ZXzknhybDxD4/3RKwIPncJ1fcN55bwkAgw6nluV7zLN26byyfYSthcZuXtIFIPiXC9j5nSVU/h0e9tItBXUWFmfW8ekzsF4e3Rs96JOEUwfHoOvp46Xfiug3Gg77keiQ5X8erCKO3/O5MMtJQyID+K+YdGtliTU0HBnmtUlX1JSQlZWFp06daK6uprgYGd1KCgoiOpq54d6RUUFYWHHfkmGhoZSUVFx9NwjLF26lKVLlwLw4osvHncfjZah1+u157EJSFWlYsYt2I9sqvoDERBE8PPvoo9NcOl8rlgXe2425YW5+F84FZ8WjiWvvoXyDavODfM2AAAgAElEQVTg6w8IHTYG4dm6y87WvduptFoJGDwcrw5+3dknX0r5vK/w2bUJ38tuOO5YzfcfYyrMxe+maRj6DUEXnwyqStVT92L78j8EDR6OPiqW2sLDGD0NhPUZwIL95ZTU23hwXGfCw9tuh31YGPTdWcGCA9XcOLwz+hMvp4eFQd+P22z+P1NWb2XmLzups6q8/rc0ukc13O8aFgYDOsUwc1E6H20toWtsKCNSQl0WR2Pvl7VZFczbX8nlfaK5akiqy+b7M2HAdQOtfLD+MAVWT3rHuLZN4KOdB9HrBDcMTSXUt30q/6ciDJh1vjf3/rCLm388RJC3npRQX5JCfNiYU0letZkekX48dl4S5ySH4nB0XNuMRsNo3/uupclJsdlsZvbs2dx00034+BzfXyeEaPblxQkTJjBhwoSj/19W1rZ9XH8FwsLCtOexCagbViEz0xETLoGIaOeNUkX+/CXlLz6M8sgrrU4Y/4wr1kVd5lRXqO/SC2MrxpJX3oo650lKv3gf5cKrTn1uYZ6zelqQg8zPgfzDYDYeO8FuByGojUqkrqNfdx5e0Kk7dct+wThq8tHPI1lahPrrT4gREzENn4gJoLwcgOB/PkHZP6+n/NUnUGa8gLpnOyR1oryyiq+2HCYxyEAXf0ebv6cmpfjz4m81LNyezdAOMkKoMtt57NfDlBltzo1regtlZaeW7LprQCh5FfU8tTCdl85NcJnVb0Pvl3KjjVmLs0kONnBld/82XZOJiV58v0PPnOUZvHReossqo+VGGwv2FjMmKQBpqqGs9ftCXUK8F/zf5CR2FxvJqbKQU2Vhwd5aov09eGx0LINi/RDCjsPR9u8Fjeajfe+3jJiYmAZvb1JSbLfbmT17NiNHjmTIkCEABAYGUllZSXBwMJWVlQQEOH9Rh4SEHLdA5eXlhIRoWoYa7oG025E/fQ5xSYgr/n6ca5kMj0J94xnkV+8jbrinXeJRl8yFyrI/YmnAqvlIbFvXQmo3RFDrKnKiR1/EwBHIBd8hh4xBhEc1PN++HahzngQpQa+H6HjnZjq/EypnMQnNUsJoS8TQsU7r68OHnPJlgJz3JSg6Z+vICejCoxDX3Yl8/1XnayI3EzH+Yg6Wm8mstHD7wMh2UXsYHOdHhK8H89IrOiQprrE4eHJZLiX1NmaOjad7eNM2FRr0Co+OjmXGohyeXZnHq5OSCPJ2vUSXQ5XMWVuIxa7y4PAYPNvYLMJLr3BD33BeX1fIrwerOc8FNsZ2VfLK7wUoAi7r6bqquqtIDvYi+U8/aqSU7a50oqHhDpz200VKybvvvktsbCwXXnjh0dsHDhzIqlWrAFi1ahWDBg06evvq1auRUpKRkYGPj89JrRMaGh2FXLsUSotQplx/XEIMINIGIiZfjvxtCeq6U2/acgXq+pXIb//rdC7731tItWHNUFlaBLlZzVadaAxxxc2gKKhfN7zpTkqJ+tPnEByK8vS/Ud78Bt2Tr6PcMh3lyluP/xvpPpbDYsAI0Hsc7ROW+YeR61cixl6ACG44EVEGj0KcMxa58Duw2xEpXVlysApPnWB0cvvssNcpggu7BrOnxERmRevtd5tDncXBU8sOU1hr5fExcfRsphZzqI8Hj42Jo8bi4PnVeVgdrje/+GFvObuKjdw+KJI4FylNnI6xyQH0ivThk20lVJpaL9H2ybYS9pWauHtINNH+Hd82cTq0hFjjr8ppk+L09HRWr17N7t27mTFjBjNmzGDr1q1MmTKFnTt3cu+997Jr1y6mTJkCQL9+/YiIiODee+/lvffe49Zbb23zB6Gh0RSk1YKc9zWkdoPeAxs8R1xyLXTpifzsbWTB4baLJesA8pM3oWsa4vwrkGuWIr/5sEHNULl1rTO2/kNdMrcICUNcdDXs2IjcsfHkE/Zuh0P7EedPRcQkIPRnhkC/8PVD9BmM3Lgaabej/vw5GLwQky479f2u+QeERQJgSujM6uwahif44+fZeOXe1YxPDcRLL5iX3n4b1+qtDmauyOVwtZVHRsW22MI6NcSL+4fFkF5m5s11Tde9bQrbCuv5YmcZIxP9GZ/S/A2mLUUIwZ2DI7E4JB9uKW7VWGsO1/Dz/kou6BrMqCRNykxDw5057bddt27d+Oabbxo89uSTT550mxBCS4Q13BK5ciFUlaPc+kCjlRCh06Hc9iDqM/ehvvsSyqOvIrxcJ/sEIKsqUN9+DgKDUf7xEPj5O3V2l/4M3j7OxPzP529ZC4mdEH8kbq5AjL8IuWYp6pf/Qene52gPtZQSdd6XEBKGGD7eZfO1F2LoOOSWNchfvoGt6xAXXY3wP3UiIrx9UO5+FLl3B2ur9ZjsKud2al+5LD9PHeNTAll8sJob+toJboM2hD9zROs3s8LMw6Ni6R/TuhaYoQn+XNcnjM92lBEX6MmVLpDtWplVzRvrCokPNHDn4Kh2r17GBRi4olcoX+4sY1xKXYueo7waC2+sK6JrmBd/7xfRBlFqaGi4Es3RTuMvgTQZkQu/hR79Tms0IYJCUW57EApzkYt/dG0cNivq28+DyYhyz2MI/wDnRtWptyBGTETO/xr1h/+hLpuH+vEbOJ6dDlkZLqsSH0Ho9SjX3gHlJc7WgSP8uUqsb51LWIfQsx/4ByLnfwV+/oiJlzTpbiIuGeXcKSw5WEVcgCfdXah/21Qu7BqCXZUsOtC2EpZmu8pzK/PIKDfx4IgYBse5po/58p6hjEkO4IudZfyeU9PicaSU/LC3nDlrC+ke4cMLExPwbceq/Z+5rEcIsQGevLupGIu9ea0hZrvKS6vz8dQJZoyIxUOntSRoaLg7WlKsccbRWO/tKe+z9Geoq0X523VNOl907+N0gVs2D2l0jbWttFhQP5gNWRkoN9+HiEs+Np8QiOvvQgwaiVz4HfKr95E7N4GPL2Ly5YjxF7kkhj8juqY5LZIXfY8sLjjjq8TgTPbF4FHOf0++HOHd9B7Z7Eoz6WVmzu0U1CE9lTEBngyK9WVeeiUVLuhjbYh6q4OZy3PZW2ri/mExDEtw3eV8IQT3DImiW5g3r68r5EB58+UVVCl5fXUWn2wrZXiCPzPHxnVYQgzgoVO4a3AUxXU2vmqG012lyc7Ty3PJrbbywPCYVttQa2hotA+6mTNnzuzoIABqa9vfHelsw8fHB6PRePoTz2CklKgv/gsydkO/c06bvEhjHXL+18hFP0DfIShNrBwCiKg45LKfwcOjVQYVPj4+1OdkOtUcMvYgpt6CMmLiyfMJxfmY+gxGXHItyiXXoAwdh+jep+36elO7I1ctQuZnI/wDkQu/Q1x2E8ofrm5nJNEJIARi0mUIXePP24nvl2/2lJNVaeG+odEY9B1TL0gJ8WJBhtNRbUSiv0uT8xqLg6eW53KowsyDI2IY2QZWvTpFMCjOj9+ya1mZXcOIRH98PJqW1BbWWpmzppBlByu4qFswdw+JQq90fN0mws+D0nobiw5U4euh0CXU65Trsq/EyBPLcymus3HfsBiGxHeMzJ6r+St8v5yJaOvSMvz9G35faknxWcRf4s1xaB9y/teQlw0+fohGkjdpsSB//Qn53suwdzti4AiUq29HGJp+WVwEBiPzs2Hjb4jRk1pss6vbvxPTSw+DqR7lzkdQho5tfE6hIIJCXd7H3Oh8Xt7g4QkrFiD3bQdfP5Sb7j2lPJy7I3x8ET37nzIhhuPfLxa7yuvrChkc68fYlI6z3w0w6PFQBL9kVBEbYCAxyDVqC5UmO08uzSWvxsojo+M4pw0TNS+9Qp8oXxZlVLEhr47OoV6E+jReKTXaHHyxs4w5awspN9m5d1Qyf+sS4FbOaWlRPuRWW5mXXklBjY3+Mb4nGa1IKfklo5LZawoIMOh4Znw8faLbzg2xvflLfL+cgWjr0jIaS4rPjG3lGm6BlBJOdDRSlJOkzdo0hnUrwNMAXXohv/sImdwFkdrt+HN2b0X9+A2oroC0gSh/ux4Rn9zIiKdGufAq1K3rkEt/Rlx8TfNitViQS36kat6XEJOActejiCNmIW6EGHsBcs0yyMtCXHfXmdlL3ErWHK6l3tr+G+wa4uJuIazLreU/m4vpHenTau3fMqONJ5bmUm608cTYuBarTDSHxCADj46OZc7aQv61OIfzuwRxbZ/w41ohai0O1uXW8sXOMipNdsalBHJ933C6xEe5nRmBj4eOh0fF8v2ecj7fUUZOlYWHR8US6edBTpWFPSVGNhfUs72wnkGxftw3LLpd1Us0NDRcg5Cu1M9pBQUFBR0dwhlPWzrbSNWB+vwMyDl4wqSRKPc/0y7JnrRZUR+40dlecNVtqLPuAylRnpiD8AtASumsDn/3McTEo1x7J6Jzj1bP63jnBdi3E+XF9xE+p9+BLqvKkSsWIFcvgrpavEZOxHrlbQiDaxy/2gKZl41cuwzxtxsQHn+NpPjI+6Xe6uDu+VkEeen4v8lJblGhzK22cP+CbAbG+vLQyNgWt1EU1Vp5YlkudVYHT46Jo3tE83SIW4vR5uCzHWUsSK8k2FvP1b3DKDPa2FZQz8EKM6qELqFe3DYwki5hzqsj7u7Qta2wntlrCrA5JIoAo825xyHCV895nYO5tEeIW7yGXI27r8tfFW1dWkarHO00NOT6VZBzEDFmMhxxVZMqctk81P97AuVfLyJC2th/fcdGMNUjho5F+Pqh3PEQ6ksPof73NZQ7HnJqC69bAf2HOTeyuSgJbWq1WNZUOc04Nv0OqsPZwzzhYgKGjqb8D2thd0XEJSGm3tLRYXQIn+8opcpk59FRsW6TzMQHGri6dxj/217KmsO1jGhB/29etYUnluVic6jMGp9Ap9D2/1Hm46Hj9oGRjE0O4O0NRby1oQhFQOdQL6b2CqVftB9dwrzc5nlvCv2iffm/SUn8b3sJPh46ekR40zPCR9tMp6FxFqAlxRqnRdptyJ+/cGrlXnPHcVUrmTYQ9dXHUOc8gTLjBURA211+VtetgKAQ6Obc9CaSOiOm3oL84j3Ux/4BVRWIi69BXDDVpS0dIj7ZqUSxdB5ywsWNVovlV+8jt61HjJmMGH/RUQtlV26WOlRh5pNtJRTWWvHSKxj0Ct56hcQgA1f1DtMu2TaTjDITCzKqOL9r8NFKpbswpbuzjeK9TcUEeenp1Qy3uaxKM08ty0UIeG5iost6k1tK51BvXp2UREaZifhAA36GM/t1GuHnwYMjYjs6DA0NDRejbbQ7i3BVw72Ukh1FRj7YUsyPeyv4aVs+PwT05puIYWRUWOgR4X10R7kICkF06oFc+Qty1xbEoJEIT9fbmMqaKuQX7yJGTULp2f/YgaTOUFwAedkot89AGTO5TeS0jipRqCqiZ7+T48vKQH79AeL8K1AuvwnheyxxdsW6VJntfLC5mHc3FmOyqfSO9MXHU0ERAotdsj6vlhWZNUT7exAX0LEJ0JmCp5c3jy3MQAjBI6Ni8dB1vNLBn1GEoHu4N7/l1DA/vZLsKjOdQrzwP0VCWW22syKzmtfWFeKpV3huQiLx7WSNfDoUIQj39cDzNMoe2sYh90RbF/dEW5eWoW200zgtUkq2Fdbz1a5y0stMhHrrSQnyxJB5EC9PPSI5gVXZNUybn8Xf+0cwITXQqa/bpSfKXY+h/nsW6htPo0yf5fL+WbnpN3A4ECcoNwgh4JbpCKsZ4dV2/ZIiPhkx8lzkr3ORvQcdZwAipUT99r/gH4g4728undehSuanV/LVrjIsdpWLuwUzNe3kivDBcjNvri/k+VX5jEoK4LYBEQR4aW/vU/Ht9nyyKi08PDK2ybJh7U18oIG3L0rhp/0VfL+nnHvyMzm/SzA9wn3w9lDw0it46QWZlRZWZ9ewo6geVUJysIFHRsUS6ef6H6gaGhoaZyvat6YGAHtLjHy0tYSMcjNhPnruGBTJhNRAdL/+iNzxCcpDLyI6RXNZz1D+vb6Qf28o4recGu4eEkWknyeiZz+nPfI7LyJX/IKYdJlL45PrVkBCKiI28aRjQlGgDRPio/NMvQWZvgv1v/+H8tQbx9oodmyEA3sR197p0sS8uM7KnLWF7Cs10T/al1sGRBDXSNWvU6gXr05K4vu95Xy7u4wdRfU8My6epGD33dzXkZTU2fhg3WEGxfpxTnzrLI7bGoNeYWqvMCakBvH5jlLm7a/k5/0nu95F+nlwaY9QRib6kxhk6BADEg0NDY0zGS0p/otTbbbz8bZSlmdWE+aj567BUYxLCcRDJ5DGetSF30PaQEQnp4pDtL8nsyYksPhAFR9vK2X6wmxemJhIQpAB0X8YdE1DrvgFOXEKQuea6pvMP+zc5Hdlx24EE17eKLc+gPriv5CfvQO3PQiqivr9JxAVi2jAkKMlSClZkVXDfzYVIwTcPyya0UkBp01yPHSCq9LCOCfOj2dW5PHMyjxeOS/xlBqxHU291UGV+XiZP3+DjoA27Dl1qJK3NhYhBPxjUOQZkzyGeOuZdk401/YJp8pkx2xXMdtVTDaVcF8POp/GVEJDQ0ND49RoSfFfFFVKfj1Yzf+2l2CyqVzWI4SpaWF4/anfTy75EYx1KFOOt0ZWhGByl2D6RvvyyJIcnl6Ry8t/JF/KhItR33oOuXUdYtCIVscppUT+ttiph/yHfW9HIpK7IC6+Bjn3M0gbCBYzFOWh3P2oS1zn6iwO3t5YxJrDtfQI9+a+YdHNvgSeFOzF42PieOTXwzy7Mo/nJybi7eEe/bImm8reEiO7io3sLDaSVemU5fozioBBsX6c2ymIftG+6BTXJnofbi1he2E9M8alnpGKASHeekJaqV2soaGhoXEy2idrG3JEN5e87OMPxCUiJk7psKpOndXBK78XsL2wnl6RPvxjUCQJJ1yWl3U1TgmyQSMRCSkNjhPt78mTY+N55NfDPL0ijxcmJuDTexCERyGX/gStSIqlzYrcsAq59GfIz0EMGI4ICG7xeK5ETL4MuXsr8ot3nW5wnXtAnyGtHndnUT2vrSukymTnuj5hXNojtMUJYUqIFzNGxPDcqjxe/T2fR0fHuTy5bCoO1dmrvjyzmo15ddhUiV6BrmHeTO0VSrS/J3+OLKvSwvLMajbk1RHmo2diahCXdA9xSWI/b38Fv6RXckm3YKakRWv6nhoaGhoaR9GS4jZELvjWWVEMCoUjrQQOB6xbDpUVMPXmdk+MC2utPLsyj8JaK3cOjuS8TkENxiCXzQeLGXHhlaccLyXEi0dGxfLMilxeWJ3PU2Pj0I2/GPnVf5CH9p/kNnc6ZE0lcuVC5MqFUFsNsYmIG6chhoxu1jhtiVB0KLdOR336XqitRpn2RKvW0eZQ+XxHGXP3VRDt78lL5yXSObT18mADY/24fWAk724q5oMtxdw+sH1bBUrrbcxPr2RlVjVVZgf+Bh3ndg5icKwf3cO9MTSiQjA6Ga7tE87G/FqWHKzmq11lrMiqZvrwGLq2QjZtfW4tH24p4Zx4P27qH9HicTQ0NDQ0zk60pLiNkFvWIOd+hhg8GnHr9KPJiJTSqWe79Cfw9m62dXBr2FNs5IXf8kFKnhmf0KjuqTQbkcvnQ99zEDEJpx23b7Qv086J5rV1hbyxroj7ho2Dnz5HLpvX5KRY5mYhl/6M3LgK7HanPfPES6Bbb7fskxShESh3P44szkMkd2nxOHnVFl5dU0BWpYVJnYP4e/+I41pYWsvkLsEU1dmYu6+CCF8P/tYj1GVjN0a12c53e8pZmFGFKiUDY/0YnxJI/xg/PHRNW0sPnWB4QgDDEwLYU2LktbUFPLwkh6m9QpnaK6zZVe8D5SZmrymgU6gX04fFnFFmERoaGhoa7YOWFLcBMusA6n/nQGo3xE3TjkvqhBBw5a1gNiHnfYXq5Y1yrmtlvE6Kx2Jh2Xuf8E7ICKJ8dTw+IZlo/8b7VOXqxc5e4vMvb/IcY1MCKTfZ+XR7KZF+HlwzcqIzyS2/CREa3vhcJYWon74F+3eCpwEx4lyn8UWU+wvji669jpNmay77So3MWpmHXggeGx3L4LiGdRNby439wimtt/HxtlJ8PXWc26ltDFaMNgdz91Xw075KrA6VscmBXJUWRoRf6/p2e0b48Nr5yby/uZivdpWztaCefw6NblSJ40T2l5p4fnUewd56Hh8d12iFWkNDQ0Pjr42WFLsYWVGK+taz4B+EctejCI+Tk0+hKHDjPWAxI7/9yJkYj5rUZjGt/W4+/w4ZQVpNFjPW/A+/8sHIS6496rh2XPw2G3LJT9C9T7MroJf1CKGw1sq3e8pJ6DOR4b/+7JRnu/ymRu+j/vAJZB9AXHYjYuS5CF/XJoZSShwSbA6Jl164TdV5c34dL/2WT5iPB0+Pi2914ngqFCG4f1gMJlseb28owluvMDKp+bbBpyK32sLzq/IoqLUxLMGfa3uHNTlpbQq+njruGxbDwFg/3tlYxLRfspiYGsRVvcMa3XTmUCXf7y3ny51lhPt68OSYOIK0DWoaGhoaGo2gfUO4EGk2ob75LFjMKPfPOqXlsVB0cOt0pNWC/OwdZHg0onsfl8eUsWkHrzk601lXx2PXjcRzSSly+Xzk5t+dFdlLbzxOOk2uWwbVFSi33N/suYQQ3DEoioIaK2/uNhI5cBKdfluMvPBKhNfJvaCyogy2rUdMnILSCl1ji10lu8pCdqWFrEozmZUWCmqtWOwqNofkiLiBl14hNsDz6F+/aN9W9ai2lFVZ1by+rpCkYANPjo0nqB1MNjx0godHxTJzeS5z1hbg7aEwMNY1+ryb8+uYvaYAD53g+QkJ9GyGHXFzGZEYQK9IH77ZXc6iDGe/8iXdQ5jcJZhAg+5oW0WZ0cactYXsLjYyKjGAOwZH4qtZYGtoaGhonAIhpZSnP63tKSgo6OgQTkJdswz0ekT33qdVPpBSor77Emxbj3LvE4heA5o0h7RYUGfdBw47ysw3W+UEFxYWdtxu+pKyKmbMP4indPDyZWkEBziTFVlVjvz5S+RvS6DvEJTbHkR4GpAOB+oTd4KvP8qjr7a4qlpttvPgomzsVhsvr3qO0L9dgTLhkpPOU3/8DLnwW5Tn/4MIi2zy+OVGG7uKjWSUmUgvM5NdZcauOo/5eCgkBxuIDzTgrVfw0Ak8dAK9EJSb7OTVWCmosVBab0cCvaN8uKpXWJsmckfWRUrJvPRKPtxSQq9IHx4b3f5OavVWB48vPUxejZUnxsTRO8q3xWNJKflhbwWfbi8lOdjAo6Pj2lXirLDWymc7Svk9x2kRLwC/PzSOq0x2HFLyj0FRjE1uWOP5xPeLhnugrYt7oq2Le6KtS8uIiYlp8HYtKW4Ede1y5EevHbshNhHRvQ9i4IgGN4+p879G/vQ54vK/ozTT6ldm7EZ95VHEhEtQWmFQ8ec3h9Hm4OGvtlCqevLSQB8Sep7cCqEun4/86n3o1B3lnsedMmPvv4py5yOI/kNbHAdAdqWZh5bkEGcqZdb29/Ce9W+Ef+DR49JmQ33oZkjpiu6ex5s0ZpXZzre7y1l0oBK7Cl56QadQb7qGetE51JuUEAMRvh5NSuaNNge/Hqzmh73lVJkd9Irw5qreYaRFtjxJbIywsDAKikt4Z2MxyzOrGRrvx/ThMXjqOqa3tdps59FfD1NQa+WSbiFc3TusWX22Ukq2FNTz/Z5y9paaGJ7gzz+HRndYr25mhZm9pUZqLA5qzA5qLA6EgGt6hxMb0HjvvPZl4p5o6+KeaOvinmjr0jK0pLgZyKJ81Gfvh8RUlMtvRu7fidy3HQ7uA5sVcd6liCnXIvTOqpjcvh71recRQ0Yjbpneogqr+vk7yFWLUR5+CZHStUVxH3lzWOwqz83fw646hSe8D9H/sosbn3fT78gP/w+iYkF1lluVmW86+55byYbcWl5YnU+Xmhwe8M4h8qbbj827fgXywzko9z+D6NH3lOMc28BVgdUhmZAayOTOwSQGGVqtvWuxqyw5WMUPeyuoMNmZkBrILQMiXFrBVQ3+/OunXRwoN3N1WhhT00I7XP2g3urg420lLDlYTYy/B9POiaZHxKmr5XZV8ntODT/srSCnykKYj57LeoYyuXPDsn7ujvZl4p5o6+KeaOvinmjr0jK0pLiJSJsN9cUZUFGK8sTriJCwY8csZuQ3/0WuXgTJXVBufQBsNtQXZkBULMq/XkB4tmxzkTQZUZ+6B7x9UJ6YczThbg5hYWHkFpbw3KIMdldL7i5dyYRpt552LLl3O+rbL4DFhPj7fSjDxrXoMTTEmpwa3vz9MDqblWk9fThnkLPK7nj+QTDVozzzdoMJlSolu4uNrMiqZu3hWsx2yfAEf67pE0ZcgOs2cB3B6lD5elc5P+wtP2qn2ze69VXjvSVGXv69EJPNwfRh0QyJbxuFiZayvbCetzYUUVpvY2xKIElBBgK9nC0I3nqF/ForWZUWsivNZFVaqLepJAR68rceoYxKCkDfQYYgrkD7MnFPtHVxT7R1cU+0dWkZWlLcRNSv3kcum4dyz+OIPoMbPEduWYP6v387K6s+vmC3ozz2f8cl0C1B7tyE+uYsxEVXo1x8dbPv7+UfxD8/XUdGncq0giWMvfU6REjjcmjHzX34EHLbBsQFU11iV/xnCsqqefXHrRzyiebCLkHcGFKN7sUZiKtvRxl3oXN+KSmus5FVZSGjzMRv2TWUGu146xWGJ/ozuXMwnUJb3m/dVNLLTLy+rpD8GiuTOwdxQ7/wFlWN660OvtxVxi/plcQGevGvEdEnuQa6Cyabyqc7Sll8oAr7iZ7LONtUEoMMJAV5MTjOjwExvmdkZfhEtC8T90RbF/dEWxf3RFuXlqElxU1A7tiI+u9nEeMvQrnqtuOPSUl+rZXSejteeoFXfRWG7z7EP3svfvc9gejUwyUxqO/PRm5Z46wWxyY2HKfDAdWVxyXhNRYHsxYd5FCNnemFixl++02IoBCXxOQKLOtX8b+VGcyPH4mPtOFvqcU7MhJfgx6HCp/GxIsAAAz0SURBVDlVFkx/7JZTBPSJ8mVcSiBD4vzavVfVYlf5YmcZP+2rIMhLx039Ixid1PBmrRORUrIqu4aPt5ZQZXZwXucg7hvfDUttVTtE3jpUKTFaVaotDmrMduptKjH+nkT5e3R4u0dboH2ZuCfaurgn2rq4J9q6tAwtKT4FsqbKacm8aiHEJKA8/ArCwwOTTWVXcT1bC+rZWlhPcZ3tpPsKIDHIQM8Ib3pG+tAz3IdAL12LK2myttrZRuEXgPLY7JPUKKSUyPdeRm5ZA6ERiB59KUjtz8sFARSYJTOKljD4H7ecUg6uI5BSor76GFtrdWz2T8UUk4wpLhWj1YEEEgINpIR4kRRkIDHI4BYGCwfKTby3qZgD5WZ6hHtz28BIUkIarlY7VMmOonq+21POnhITnUO9+MegSDqHemsfWm6Kti7uibYu7om2Lu6Jti4tQ0uKG0Aa63Es/pGaVUupVLyo6juKvLRRZBoVDlWaya+xokrn5ePeUb70j/YlMciA2a7+8Scprbext8TIvlITFofzqRQ4NXG99AIvD4VAg55QHz1hPnpCfTyIC/CkV6RPo4mf3Lsd9bWnEOeMQfz9vuMSbHXxD8jvPkYMHYfDZGRhlTefxo/HQ7XzUOVqet92M8LPtcYMrkLm56A+809QVWcvcXRcR4d0WlQpWZ5Zzf+2lVJrddA93JvUEC9SQ7xICfbC4lBZlVXD6pwaqs0OAgw6ru8bzoTUwKPVVe1Dyz3R1sU90dbFPdHWxT3R1qVlNJYU/2XNO/Zt3c3LO4xU6/vhGDTw2IG9dYR460kNMTA03p9ekT70CPfG4zTyWXZVcqjCzP5SE3VWBya7isWuYrKpVJsdZFWa2ZRvx/pH4uypE6RF+jAw1o+BMX7HOZqJHn0RF16JnPcVdOmFGDERALlvB/L7/8GAYZRccRf/3lDErmIj/YPgLr9yut75MBUms+ufLBchYhMRl94A5aVnREIMTje4CalBnBPvz497K9hVXM+iA1VH1xFArwgGxfoxJjmAATG+p32taGhoaGhoaLgff9mkOCgujr47NxCSEERwVBjBXnqCvPXE+nu2yApWrwi6hnmf0iFNSkmdVeVghZnN+XVszq9jS0Ex71FMpxAvRiT6MyIxgHBfD2dSfHAf8ov3kEmdwMcP9T+vkB/fk9WDbmDegmwEcM+QKCakBiJENxRfP3DjpBhAOe/Sjg6hRfh5OivAEI5DleTXWDlY4XyuB8f54ae5pWloaGhoaJzRnLZ94u2332br1q0EBgYye/ZsAOrq6pgzZw6lpaWEh4dz//334+fnh5SSjz76iG3btmEwGLjrrrtISUlpUiDusNGuvTmyeW9jbh1rDtceTbK6hHqREuJFiGIjeMk3hGIh3y+KlYZEDvnFoggYGOvHbQMij6swa5dR3BNtXdwTbV3cE21d3BNtXdwTbV1aRovbJ8aMGcOkSZN46623jt42d+5c0tLSmDJlCnPnzmXu3Llcd911bNu2jaKiIt544w0OHDjABx98wPPPP++6R3GWIYQgLsBAXE8Dl/YMpbDWypqcWtbn1bImp4ZaqwqJFxw9P8Vg5+aeEYxMCiCkBdVsDQ0NDQ0NDQ2NhjltZtWjRw9KSkqOu23Tpk3MnDkTgNGjRzNz5kyuu+46Nm/ezKhRoxBC0KVLF+r/v717C4mq3cMA/qxxVJLUZsbQneYuI8PoNGhiF3VRFkUEQl8SFXSig4pCB9KLIhGkJsgsUrDoyouyiywIIjFD6cKKBsocS/AQHWSmPTONoo4247sv2t/0uclv7xmXreWs53fnsGb4vz4u1t+13nnf4WG43W4YDIYZKT7c/CM2Cn+sMOGPFSYAPzaUcI348C/rK8T6R/HPjfJtqkFEREREP4V0u9Hj8QQa3Xnz5sHj8QAAXC4XEhJ+rp1rMpngcrl+2RQ3NzejubkZAHDx4sVJ76OfFgDA4l/f5v9ver2ev0cVYi7qxFzUibmoE3NRJ+Yir2k/g5ckKaQ1eXNzc5Gbmxv4mXNipo9zi9SJuagTc1En5qJOzEWdmEtopppTHNLaUfHx8XC73QAAt9uNuLgf6+IajcZJ4TidThiN6tlVjYiIiIjoV0JqirOystDa2goAaG1txdq1awOvt7W1QQiB7u5uxMTEcD4xEREREane/5w+UV1dDZvNhqGhIRw/fhz5+fnIy8vDlStX0NLSEliSDQDMZjOsVitKSkoQFRWFwsLCGR8AEREREdF0aXqb53DDuUXqxFzUibmoE3NRJ+aiTswlNLLOKSYiIiIiCidsiomIiIhI89gUExEREZHmqWZOMRERERGRUninOIyUlZUpXQL9AnNRJ+aiTsxFnZiLOjEXebEpJiIiIiLNY1NMRERERJoXUV5eXq50ESSftLQ0pUugX2Au6sRc1Im5qBNzUSfmIh9+0Y6IiIiINI/TJ4iIiIhI89gUExEREZHm6ZUugKZWW1sLq9WK+Ph4XL58GQDQ39+Pmzdvwuv1Yv78+SgpKUFMTAwcDgdOnDgR2M976dKlOHr0KACgt7cXNTU1GB8fh9lsxsGDByFJkmLjmu3kyGVsbAxVVVWw2+3Q6XTIzMzE3r17lRzWrCfX+fIni8UCh8MR+CwKjVy5+Hw+3Lp1CzabDZIkYffu3cjJyVFsXOFArmyePXuGxsZGSJIEg8GA4uJixMXFKTau2S6YXADgw4cPuHHjBkZHRyFJEi5cuICoqChe+0MhSLU6OztFT0+POHnyZOC1srIy0dnZKYQQ4smTJ+L27dtCCCHsdvuk4/6qrKxMvH//XkxMTIjKykphtVpnvvgwJkcuXq9XdHR0CCGE+P79uzh37hxzmSa5zhchhGhvbxfV1dV/ewz9f+TKpaGhIXCc3+8XHo9nhisPf3Jk4/P5xOHDhwN51NfXi4aGht9QffgKJhefzydOnTol+vr6hBBCDA4OCr/fH3gPr/3B4fQJFVu+fDnmzp076bUvX74gIyMDALBq1So8f/78bz/D7XZjdHQU6enpkCQJGzZswMuXL2esZi2QI5fo6GisWLECAKDX67F48WI4nc6ZKVgj5MgFALxeLx4+fIidO3fOSJ1aI1cuT58+RV5eHgBAp9PxTqQM5MhGCAEhBMbGxiCEwMjICIxG44zVrAXB5PL69WukpqZi0aJFAIDY2FjodDpe+0PEpniWWbhwYeAPu729fVIj5XA4cObMGZw/fx5dXV0AAJfLBZPJFDjGZDLB5XL93qI1INhc/mp4eBivXr3CypUrf1u9WhFKLnfu3MGOHTsQFRX12+vVimBzGR4eBgA0NDSgtLQUVVVV+Pbt2+8vXAOCzUav1+PIkSM4ffo0jh07hs+fP2Pjxo2K1B7OpsplYGAAkiShsrISpaWlePDgAQBe+0PFpniWKSgoQFNTE0pLSzE6Ogq9/se0cIPBgNraWly6dAn79+/HtWvXMDIyonC12hFqLn6/H1evXsW2bduQmJioVPlhK9hc+vv7YbfbkZ2drXDl4S3YXPx+P5xOJ5YtWwaLxYL09HTU19crPIrwFGw2Pp8PTU1NsFgsqKurQ2pqKhobGxUeRfiZKhe/3493796huLgYFRUVePHiBTo6OhSudvbiF+1mmeTkZJw9exbAj8cpVqsVABAZGYnIyEgAPxbyTkxMxMDAAIxG46T/9J1OJx9tzYBgc1myZAkAoK6uDklJSdi+fbsyhYe5YHPp6elBb28vioqK4Pf74fF4UF5eDu5xJK9gc0lLS0N0dHTgn5WcnBy0tLQoU3yYCzYb8Z+tDpKSkgAA69atC9ytJPlMlYvJZEJGRkZgOpHZbEZfXx/Wr1/Pa38IeKd4lvF4PACAiYkJ3Lt3D5s3bwYADA4OYmJiAgBgt9sxMDCAxMREGAwGzJkzB93d3RBCoK2tDVlZWYrVH66CzQX48Zh+ZGQEBw4cUKRmLQg2ly1btqCurg41NTWoqKjAggUL2BDPgGBzkSQJmZmZsNlsAIC3b98iJSVFmeLDXLDZGI1GfPr0CYODgwCAN2/eIDk5WZniw9hUuaxevRofP37E2NgY/H4/urq6kJKSwmt/iLijnYpVV1fDZrNhaGgI8fHxyM/Ph9frxePHjwEA2dnZ2LNnDyRJQnt7O+7evYuIiAjodDrs2rUrcAL09PSgtrYW4+PjWLNmDQ4dOsRlWaZBjlycTicKCgqQnJwceAy2detWbNq0ScmhzWpynS9/cjgcsFgsXJJtmuTK5evXr7h+/TqGh4cRFxeHwsJCJCQkKDm0WU+ubJqamvDo0SNEREQgISEBRUVFiI2NVXJos1owuQBAW1sb7t+/D0mSYDabsW/fPgC89oeCTTERERERaR6nTxARERGR5rEpJiIiIiLNY1NMRERERJrHppiIiIiINI9NMRERERFpHptiIiIiItI8NsVEREREpHn/BjJxUeyx/qVhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Since I applied log operation before differencing, its effect had to be restored so exponents had to be taken\n",
    "predictions_ARIMA = np.exp(predictions_log)\n",
    "plt.plot(ts)\n",
    "plt.plot(predictions_ARIMA)\n",
    "plt.title('RMSE: %.4f'% np.sqrt(sum((predictions_ARIMA-ts)**2)/len(ts)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "09I1W18zuBAV"
   },
   "source": [
    "### Now Prediction isn't nice, something seems to be missing and we can easily see a seasonal characteristic which we didn't encounter earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "OFAQAI_Am2x3",
    "outputId": "021eb553-98e0-4b1d-853d-97a62e0b19a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Additive Decomposition\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose# Decompose time series into daily trend, seasonal, and residual components.\n",
    "decomp = seasonal_decompose(ts_log, model = 'additive')# Plot the decomposed time series to interpret.\n",
    "fig, (ax0, ax1,ax2,ax3) = plt.subplots(4,1, figsize=(15,8));\n",
    "decomp.observed.plot(ax=ax0, title='Our time series');\n",
    "decomp.trend.plot(ax=ax1, title='Trend');\n",
    "decomp.resid.plot(ax=ax2, title='Residual');\n",
    "decomp.seasonal.plot(ax=ax3, title='Seasonality');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "cyNSNjW4Z1ua",
    "outputId": "b3e4b549-bce4-4c24-ea36-af7b212f84a1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Multiplicative Decomposition\n",
    "decomp = seasonal_decompose(ts_log, model = 'multiplicative')# Plot the decomposed time series to interpret.\n",
    "fig, (ax0, ax1,ax2,ax3) = plt.subplots(4,1, figsize=(15,8));\n",
    "decomp.observed.plot(ax=ax0, title='Our time series');\n",
    "decomp.trend.plot(ax=ax1, title='Trend');\n",
    "decomp.resid.plot(ax=ax2, title='Residual');\n",
    "decomp.seasonal.plot(ax=ax3, title='Seasonality');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1ZrOAcsgvGB_"
   },
   "source": [
    "## SARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tV1V0dHeZ1kB",
    "outputId": "b6372f8e-e164-4e08-a045-f7fbfa8a6951"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(1, 0, 1), (1, 0, 2), (1, 1, 1), (1, 1, 2), (2, 0, 1), (2, 0, 2), (2, 1, 1), (2, 1, 2)]\n",
      "[(1, 0, 1, 12), (1, 0, 2, 12), (1, 1, 1, 12), (1, 1, 2, 12), (2, 0, 1, 12), (2, 0, 2, 12), (2, 1, 1, 12), (2, 1, 2, 12)]\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "q = [1,2]\n",
    "p = [1,2]\n",
    "d = [0,1]\n",
    "pdq = list(itertools.product(p, d, q))\n",
    "print(pdq)\n",
    "#Generate all different combinations of seasonal p, q and q triplet\n",
    "seasonal_pdq = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))]\n",
    "print(seasonal_pdq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uh7QDSMa1OHZ",
    "outputId": "32205014-48ea-4e7d-a970-b92a2cb69029"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples of parameter for SARIMA...\n",
      "SARIMAX: (1, 0, 2) x (1, 0, 2, 12)\n",
      "SARIMAX: (1, 0, 2) x (1, 1, 1, 12)\n",
      "SARIMAX: (1, 1, 1) x (1, 1, 2, 12)\n",
      "SARIMAX: (1, 1, 1) x (2, 0, 1, 12)\n"
     ]
    }
   ],
   "source": [
    "print('Examples of parameter for SARIMA...')\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[1]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[1], seasonal_pdq[2]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[3]))\n",
    "print('SARIMAX: {} x {}'.format(pdq[2], seasonal_pdq[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "I3L6ArBv1O64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.11604D+00    |proj g|=  6.86654D+00\n",
      "\n",
      "At iterate    5    f= -1.50116D+00    |proj g|=  3.53677D+00\n",
      "\n",
      "At iterate   10    f= -1.56139D+00    |proj g|=  5.54026D+00\n",
      "\n",
      "At iterate   15    f= -1.68069D+00    |proj g|=  1.71416D+00\n",
      "\n",
      "At iterate   20    f= -1.68597D+00    |proj g|=  4.83145D-01\n",
      "\n",
      "At iterate   25    f= -1.69197D+00    |proj g|=  3.55392D-01\n",
      "\n",
      "At iterate   30    f= -1.69248D+00    |proj g|=  1.31923D-01\n",
      "\n",
      "At iterate   35    f= -1.69312D+00    |proj g|=  1.83966D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     38     59      1     0     0   8.216D-04  -1.693D+00\n",
      "  F =  -1.6931228628013615     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.92986D-01    |proj g|=  5.88488D+00\n",
      "\n",
      "At iterate    5    f= -1.29034D+00    |proj g|=  2.29440D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -1.36540D+00    |proj g|=  4.67409D+00\n",
      "\n",
      "At iterate   15    f= -1.49078D+00    |proj g|=  3.49168D+00\n",
      "\n",
      "At iterate   20    f= -1.50973D+00    |proj g|=  6.60684D-01\n",
      "\n",
      "At iterate   25    f= -1.51626D+00    |proj g|=  2.77729D-01\n",
      "\n",
      "At iterate   30    f= -1.52488D+00    |proj g|=  1.41017D+00\n",
      "\n",
      "At iterate   35    f= -1.53121D+00    |proj g|=  3.42877D-03\n",
      "\n",
      "At iterate   40    f= -1.53121D+00    |proj g|=  5.36494D-02\n",
      "\n",
      "At iterate   45    f= -1.53121D+00    |proj g|=  4.66894D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     46     67      1     0     0   4.733D-03  -1.531D+00\n",
      "  F =  -1.5312128485419274     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34395D+00    |proj g|=  2.16649D+00\n",
      "\n",
      "At iterate    5    f= -1.38960D+00    |proj g|=  3.75095D-01\n",
      "\n",
      "At iterate   10    f= -1.47150D+00    |proj g|=  1.52844D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.47313D+00    |proj g|=  2.20470D-01\n",
      "\n",
      "At iterate   20    f= -1.48763D+00    |proj g|=  2.84426D+00\n",
      "\n",
      "At iterate   25    f= -1.52153D+00    |proj g|=  1.27889D-01\n",
      "\n",
      "At iterate   30    f= -1.53320D+00    |proj g|=  7.13986D-02\n",
      "\n",
      "At iterate   35    f= -1.53324D+00    |proj g|=  3.41128D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     35     51      1     0     0   3.411D-04  -1.533D+00\n",
      "  F =  -1.5332367364393895     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34927D+00    |proj g|=  6.88934D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.39152D+00    |proj g|=  5.25504D-01\n",
      "\n",
      "At iterate   10    f= -1.39455D+00    |proj g|=  2.38790D-01\n",
      "\n",
      "At iterate   15    f= -1.39466D+00    |proj g|=  1.01339D-01\n",
      "\n",
      "At iterate   20    f= -1.39581D+00    |proj g|=  1.00931D-01\n",
      "\n",
      "At iterate   25    f= -1.39743D+00    |proj g|=  9.17984D-01\n",
      "\n",
      "At iterate   30    f= -1.40361D+00    |proj g|=  7.57059D-02\n",
      "\n",
      "At iterate   35    f= -1.42412D+00    |proj g|=  7.19607D-02\n",
      "\n",
      "At iterate   40    f= -1.42495D+00    |proj g|=  2.90156D-01\n",
      "\n",
      "At iterate   45    f= -1.42511D+00    |proj g|=  5.54251D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     46     72      1     0     0   5.029D-03  -1.425D+00\n",
      "  F =  -1.4251081007061004     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.01760D+00    |proj g|=  6.19776D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.36458D+00    |proj g|=  7.61423D-01\n",
      "\n",
      "At iterate   10    f= -1.44469D+00    |proj g|=  3.71900D+00\n",
      "\n",
      "At iterate   15    f= -1.53247D+00    |proj g|=  2.22415D+00\n",
      "\n",
      "At iterate   20    f= -1.54586D+00    |proj g|=  1.05503D+00\n",
      "\n",
      "At iterate   25    f= -1.55041D+00    |proj g|=  6.85226D-03\n",
      "\n",
      "At iterate   30    f= -1.55051D+00    |proj g|=  1.41141D-01\n",
      "\n",
      "At iterate   35    f= -1.55102D+00    |proj g|=  4.28669D-02\n",
      "\n",
      "At iterate   40    f= -1.55102D+00    |proj g|=  1.23518D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     40     65      1     0     0   1.235D-03  -1.551D+00\n",
      "  F =  -1.5510188385459047     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.93644D-01    |proj g|=  5.96250D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.30844D+00    |proj g|=  1.35262D+00\n",
      "\n",
      "At iterate   10    f= -1.54243D+00    |proj g|=  4.78813D-01\n",
      "\n",
      "At iterate   15    f= -1.54654D+00    |proj g|=  3.11851D-01\n",
      "\n",
      "At iterate   20    f= -1.54791D+00    |proj g|=  9.39458D-02\n",
      "\n",
      "At iterate   25    f= -1.54961D+00    |proj g|=  1.09543D+00\n",
      "\n",
      "At iterate   30    f= -1.55036D+00    |proj g|=  1.42463D-01\n",
      "\n",
      "At iterate   35    f= -1.55061D+00    |proj g|=  4.11188D-01\n",
      "\n",
      "At iterate   40    f= -1.56284D+00    |proj g|=  2.23587D+00\n",
      "\n",
      "At iterate   45    f= -1.56905D+00    |proj g|=  3.53536D-01\n",
      "\n",
      "At iterate   50    f= -1.57085D+00    |proj g|=  2.20923D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     65      1     0     0   2.209D-01  -1.571D+00\n",
      "  F =  -1.5708454793400923     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.23964D+00    |proj g|=  1.70401D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.29204D+00    |proj g|=  4.78945D-01\n",
      "\n",
      "At iterate   10    f= -1.35350D+00    |proj g|=  6.63942D+00\n",
      "\n",
      "At iterate   15    f= -1.40481D+00    |proj g|=  6.49701D-01\n",
      "\n",
      "At iterate   20    f= -1.40600D+00    |proj g|=  1.04409D+00\n",
      "\n",
      "At iterate   25    f= -1.41419D+00    |proj g|=  6.24217D-02\n",
      "\n",
      "At iterate   30    f= -1.41445D+00    |proj g|=  2.90194D-01\n",
      "\n",
      "At iterate   35    f= -1.42080D+00    |proj g|=  3.72419D-01\n",
      "\n",
      "At iterate   40    f= -1.42403D+00    |proj g|=  2.41934D-02\n",
      "\n",
      "At iterate   45    f= -1.42430D+00    |proj g|=  5.33802D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     48     60      1     0     0   5.259D-04  -1.424D+00\n",
      "  F =  -1.4243036700436926     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.28241D+00    |proj g|=  5.81575D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.31183D+00    |proj g|=  1.11377D+00\n",
      "\n",
      "At iterate   10    f= -1.33276D+00    |proj g|=  3.09738D+00\n",
      "\n",
      "At iterate   15    f= -1.33774D+00    |proj g|=  8.07568D-02\n",
      "\n",
      "At iterate   20    f= -1.34427D+00    |proj g|=  1.65869D+00\n",
      "\n",
      "At iterate   25    f= -1.36349D+00    |proj g|=  4.39451D-01\n",
      "\n",
      "At iterate   30    f= -1.37397D+00    |proj g|=  7.93060D-01\n",
      "\n",
      "At iterate   35    f= -1.40300D+00    |proj g|=  1.25846D+00\n",
      "\n",
      "At iterate   40    f= -1.41266D+00    |proj g|=  1.56205D+00\n",
      "\n",
      "At iterate   45    f= -1.41719D+00    |proj g|=  1.58087D-02\n",
      "\n",
      "At iterate   50    f= -1.41719D+00    |proj g|=  2.35874D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     67      1     0     0   2.359D-03  -1.417D+00\n",
      "  F =  -1.4171903793274463     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.09703D+00    |proj g|=  6.70766D+00\n",
      "\n",
      "At iterate    5    f= -1.47920D+00    |proj g|=  3.52692D+00\n",
      "\n",
      "At iterate   10    f= -1.57578D+00    |proj g|=  5.86339D+00\n",
      "\n",
      "At iterate   15    f= -1.64495D+00    |proj g|=  4.38179D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   20    f= -1.65941D+00    |proj g|=  1.56880D+00\n",
      "\n",
      "At iterate   25    f= -1.67542D+00    |proj g|=  1.15845D-01\n",
      "\n",
      "At iterate   30    f= -1.67757D+00    |proj g|=  7.59035D-02\n",
      "\n",
      "At iterate   35    f= -1.67758D+00    |proj g|=  3.93005D-02\n",
      "\n",
      "At iterate   40    f= -1.67761D+00    |proj g|=  3.38933D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     42     66      1     0     0   3.066D-03  -1.678D+00\n",
      "  F =  -1.6776069783173098     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.78003D-01    |proj g|=  5.79737D+00\n",
      "\n",
      "At iterate    5    f= -1.28647D+00    |proj g|=  2.44427D+00\n",
      "\n",
      "At iterate   10    f= -1.41264D+00    |proj g|=  4.48870D+00\n",
      "\n",
      "At iterate   15    f= -1.43445D+00    |proj g|=  1.97715D+00\n",
      "\n",
      "At iterate   20    f= -1.46443D+00    |proj g|=  4.62801D-01\n",
      "\n",
      "At iterate   25    f= -1.51088D+00    |proj g|=  3.86629D-01\n",
      "\n",
      "At iterate   30    f= -1.51330D+00    |proj g|=  2.61766D-01\n",
      "\n",
      "At iterate   35    f= -1.52250D+00    |proj g|=  2.79940D-01\n",
      "\n",
      "At iterate   40    f= -1.52267D+00    |proj g|=  1.16654D-01\n",
      "\n",
      "At iterate   45    f= -1.52317D+00    |proj g|=  1.96182D-01\n",
      "\n",
      "At iterate   50    f= -1.52320D+00    |proj g|=  4.44604D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     66      1     0     0   4.446D-03  -1.523D+00\n",
      "  F =  -1.5231997372476684     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.32733D+00    |proj g|=  2.85917D+00\n",
      "\n",
      "At iterate    5    f= -1.37130D+00    |proj g|=  1.48944D+00\n",
      "\n",
      "At iterate   10    f= -1.39509D+00    |proj g|=  4.53272D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.46029D+00    |proj g|=  4.48181D-01\n",
      "\n",
      "At iterate   20    f= -1.46219D+00    |proj g|=  7.04671D-01\n",
      "\n",
      "At iterate   25    f= -1.47510D+00    |proj g|=  1.22825D-01\n",
      "\n",
      "At iterate   30    f= -1.52101D+00    |proj g|=  2.13821D-01\n",
      "\n",
      "At iterate   35    f= -1.52199D+00    |proj g|=  1.55114D-02\n",
      "\n",
      "At iterate   40    f= -1.52200D+00    |proj g|=  1.53036D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     40     51      1     0     0   1.530D-04  -1.522D+00\n",
      "  F =  -1.5219965837703220     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34493D+00    |proj g|=  6.96186D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.38786D+00    |proj g|=  6.14740D-01\n",
      "\n",
      "At iterate   10    f= -1.39164D+00    |proj g|=  8.74862D-01\n",
      "\n",
      "At iterate   15    f= -1.39322D+00    |proj g|=  6.11128D-02\n",
      "\n",
      "At iterate   20    f= -1.39459D+00    |proj g|=  1.89810D-02\n",
      "\n",
      "At iterate   25    f= -1.39497D+00    |proj g|=  5.82349D-01\n",
      "\n",
      "At iterate   30    f= -1.40842D+00    |proj g|=  1.15636D+00\n",
      "\n",
      "At iterate   35    f= -1.41500D+00    |proj g|=  2.70019D-01\n",
      "\n",
      "At iterate   40    f= -1.41691D+00    |proj g|=  4.42968D-01\n",
      "\n",
      "At iterate   45    f= -1.41801D+00    |proj g|=  2.48002D-01\n",
      "\n",
      "At iterate   50    f= -1.42396D+00    |proj g|=  1.48821D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     66      1     0     0   1.488D-01  -1.424D+00\n",
      "  F =  -1.4239644802533993     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.00859D+00    |proj g|=  6.11202D+00\n",
      "\n",
      "At iterate    5    f= -1.34727D+00    |proj g|=  2.89059D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -1.48655D+00    |proj g|=  3.67423D+00\n",
      "\n",
      "At iterate   15    f= -1.52204D+00    |proj g|=  2.99347D-01\n",
      "\n",
      "At iterate   20    f= -1.54044D+00    |proj g|=  1.82278D+00\n",
      "\n",
      "At iterate   25    f= -1.55129D+00    |proj g|=  9.37594D-03\n",
      "\n",
      "At iterate   30    f= -1.55132D+00    |proj g|=  5.25861D-02\n",
      "\n",
      "At iterate   35    f= -1.55138D+00    |proj g|=  7.90409D-03\n",
      "\n",
      "At iterate   40    f= -1.55141D+00    |proj g|=  7.47321D-02\n",
      "\n",
      "At iterate   45    f= -1.55147D+00    |proj g|=  1.25878D-02\n",
      "\n",
      "At iterate   50    f= -1.55154D+00    |proj g|=  1.23586D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     66      1     0     0   1.236D-02  -1.552D+00\n",
      "  F =  -1.5515416892081797     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.79723D-01    |proj g|=  5.89453D+00\n",
      "\n",
      "At iterate    5    f= -1.29427D+00    |proj g|=  5.18673D+00\n",
      "\n",
      "At iterate   10    f= -1.40539D+00    |proj g|=  6.26001D+00\n",
      "\n",
      "At iterate   15    f= -1.52069D+00    |proj g|=  2.01150D-01\n",
      "\n",
      "At iterate   20    f= -1.53515D+00    |proj g|=  2.49825D+00\n",
      "\n",
      "At iterate   25    f= -1.54758D+00    |proj g|=  6.55399D-01\n",
      "\n",
      "At iterate   30    f= -1.55137D+00    |proj g|=  1.10596D-01\n",
      "\n",
      "At iterate   35    f= -1.55173D+00    |proj g|=  2.67342D-01\n",
      "\n",
      "At iterate   40    f= -1.55260D+00    |proj g|=  6.07039D-01\n",
      "\n",
      "At iterate   45    f= -1.55969D+00    |proj g|=  3.79153D-01\n",
      "\n",
      "At iterate   50    f= -1.56795D+00    |proj g|=  1.61819D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     66      1     0     0   1.618D+00  -1.568D+00\n",
      "  F =  -1.5679528429338356     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.23945D+00    |proj g|=  2.09239D+00\n",
      "\n",
      "At iterate    5    f= -1.29211D+00    |proj g|=  4.86921D-01\n",
      "\n",
      "At iterate   10    f= -1.30096D+00    |proj g|=  2.46667D+00\n",
      "\n",
      "At iterate   15    f= -1.39733D+00    |proj g|=  1.30313D+00\n",
      "\n",
      "At iterate   20    f= -1.40011D+00    |proj g|=  6.75005D-01\n",
      "\n",
      "At iterate   25    f= -1.41376D+00    |proj g|=  9.60838D-01\n",
      "\n",
      "At iterate   30    f= -1.41442D+00    |proj g|=  9.45812D-02\n",
      "\n",
      "At iterate   35    f= -1.41785D+00    |proj g|=  8.18216D-01\n",
      "\n",
      "At iterate   40    f= -1.42216D+00    |proj g|=  1.52292D-01\n",
      "\n",
      "At iterate   45    f= -1.42385D+00    |proj g|=  5.81618D-01\n",
      "\n",
      "At iterate   50    f= -1.42482D+00    |proj g|=  3.51741D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     58      1     0     0   3.517D-02  -1.425D+00\n",
      "  F =  -1.4248151243265075     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.28141D+00    |proj g|=  5.84560D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.31078D+00    |proj g|=  1.05007D+00\n",
      "\n",
      "At iterate   10    f= -1.33298D+00    |proj g|=  2.82191D+00\n",
      "\n",
      "At iterate   15    f= -1.33947D+00    |proj g|=  8.36749D-02\n",
      "\n",
      "At iterate   20    f= -1.35373D+00    |proj g|=  1.40220D+00\n",
      "\n",
      "At iterate   25    f= -1.35865D+00    |proj g|=  9.54943D-01\n",
      "\n",
      "At iterate   30    f= -1.38926D+00    |proj g|=  2.08456D-01\n",
      "\n",
      "At iterate   35    f= -1.39265D+00    |proj g|=  7.77857D-02\n",
      "\n",
      "At iterate   40    f= -1.40275D+00    |proj g|=  2.35132D+00\n",
      "\n",
      "At iterate   45    f= -1.41341D+00    |proj g|=  1.30487D+00\n",
      "\n",
      "At iterate   50    f= -1.41428D+00    |proj g|=  2.27218D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     61      1     0     0   2.272D-02  -1.414D+00\n",
      "  F =  -1.4142819141016649     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.10598D+00    |proj g|=  7.00324D+00\n",
      "\n",
      "At iterate    5    f= -1.63542D+00    |proj g|=  1.90630D+00\n",
      "\n",
      "At iterate   10    f= -1.66169D+00    |proj g|=  2.68412D-02\n",
      "\n",
      "At iterate   15    f= -1.66220D+00    |proj g|=  1.88850D-01\n",
      "\n",
      "At iterate   20    f= -1.66231D+00    |proj g|=  9.71960D-02\n",
      "\n",
      "At iterate   25    f= -1.66373D+00    |proj g|=  6.41559D-01\n",
      "\n",
      "At iterate   30    f= -1.66908D+00    |proj g|=  8.11648D-02\n",
      "\n",
      "At iterate   35    f= -1.67060D+00    |proj g|=  1.56439D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     38     62      1     0     0   8.399D-04  -1.671D+00\n",
      "  F =  -1.6706009006781750     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.17059D-01    |proj g|=  6.66441D+00\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "At iterate    5    f= -1.44426D+00    |proj g|=  1.00928D+00\n",
      "\n",
      "At iterate   10    f= -1.46979D+00    |proj g|=  7.01758D-02\n",
      "\n",
      "At iterate   15    f= -1.48348D+00    |proj g|=  5.32272D-01\n",
      "\n",
      "At iterate   20    f= -1.49863D+00    |proj g|=  7.99756D-01\n",
      "\n",
      "At iterate   25    f= -1.50217D+00    |proj g|=  6.28996D-01\n",
      "\n",
      "At iterate   30    f= -1.50268D+00    |proj g|=  4.25061D-01\n",
      "\n",
      "At iterate   35    f= -1.50404D+00    |proj g|=  1.29777D+00\n",
      "\n",
      "At iterate   40    f= -1.50525D+00    |proj g|=  2.39372D-01\n",
      "\n",
      "At iterate   45    f= -1.50665D+00    |proj g|=  2.99572D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      "\n",
      " Line search cannot locate an adequate point after 20 function\n",
      "  and gradient evaluations.  Previous x, f and g restored.\n",
      " Possible causes: 1 error in function or gradient evaluation;\n",
      "                  2 rounding error dominate computation.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     48    110      2     0     0   3.761D-02  -1.507D+00\n",
      "  F =  -1.5067641037385426     \n",
      "\n",
      "ABNORMAL_TERMINATION_IN_LNSRCH                              \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.48887D+00    |proj g|=  4.34230D+00\n",
      "\n",
      "At iterate    5    f= -1.50741D+00    |proj g|=  1.42866D+00\n",
      "\n",
      "At iterate   10    f= -1.50927D+00    |proj g|=  6.33815D-02\n",
      "\n",
      "At iterate   15    f= -1.50975D+00    |proj g|=  7.86673D-01\n",
      "\n",
      "At iterate   20    f= -1.51242D+00    |proj g|=  1.13307D+00\n",
      "\n",
      "At iterate   25    f= -1.51478D+00    |proj g|=  1.31215D-02\n",
      "\n",
      "At iterate   30    f= -1.51542D+00    |proj g|=  1.12530D-01\n",
      "\n",
      "At iterate   35    f= -1.51748D+00    |proj g|=  2.49281D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Bad direction in the line search;\n",
      "   refresh the lbfgs memory and restart the iteration.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     39     78      2     0     0   9.871D-05  -1.517D+00\n",
      "  F =  -1.5174811062708371     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.35005D+00    |proj g|=  4.48792D+00\n",
      "\n",
      "At iterate    5    f= -1.39777D+00    |proj g|=  1.56067D+00\n",
      "\n",
      "At iterate   10    f= -1.40076D+00    |proj g|=  5.48421D-01\n",
      "\n",
      "At iterate   15    f= -1.40484D+00    |proj g|=  1.24054D-02\n",
      "\n",
      "At iterate   20    f= -1.40490D+00    |proj g|=  1.65499D-02\n",
      "\n",
      "At iterate   25    f= -1.40761D+00    |proj g|=  1.61997D-01\n",
      "\n",
      "At iterate   30    f= -1.40776D+00    |proj g|=  3.77534D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     31     64      1     0     0   3.655D-03  -1.408D+00\n",
      "  F =  -1.4077610809074150     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.01426D+00    |proj g|=  6.32986D+00\n",
      "\n",
      "At iterate    5    f= -1.40317D+00    |proj g|=  5.36967D+00\n",
      "\n",
      "At iterate   10    f= -1.49105D+00    |proj g|=  4.46560D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.52299D+00    |proj g|=  1.36330D-02\n",
      "\n",
      "At iterate   20    f= -1.52334D+00    |proj g|=  3.29280D-01\n",
      "\n",
      "At iterate   25    f= -1.52354D+00    |proj g|=  1.09714D-01\n",
      "\n",
      "At iterate   30    f= -1.52399D+00    |proj g|=  2.81321D-01\n",
      "\n",
      "At iterate   35    f= -1.52602D+00    |proj g|=  1.75753D-02\n",
      "\n",
      "At iterate   40    f= -1.52602D+00    |proj g|=  2.37125D-02\n",
      "\n",
      "At iterate   45    f= -1.52603D+00    |proj g|=  5.00793D-02\n",
      "\n",
      "At iterate   50    f= -1.52663D+00    |proj g|=  5.59131D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     50     64      1     0     0   5.591D-02  -1.527D+00\n",
      "  F =  -1.5266276291863583     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.25456D-01    |proj g|=  6.64540D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.43199D+00    |proj g|=  1.63473D+00\n",
      "\n",
      "At iterate   10    f= -1.47356D+00    |proj g|=  2.07512D-01\n",
      "\n",
      "At iterate   15    f= -1.49869D+00    |proj g|=  9.05633D-01\n",
      "\n",
      "At iterate   20    f= -1.53327D+00    |proj g|=  1.58455D+00\n",
      "\n",
      "At iterate   25    f= -1.53423D+00    |proj g|=  7.01090D-01\n",
      "\n",
      "At iterate   30    f= -1.53438D+00    |proj g|=  6.58073D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     32     53      1     0     0   9.620D-03  -1.534D+00\n",
      "  F =  -1.5343835546704567     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.37605D+00    |proj g|=  5.11194D+00\n",
      "\n",
      "At iterate    5    f= -1.39711D+00    |proj g|=  5.43232D-02\n",
      "\n",
      "At iterate   10    f= -1.39840D+00    |proj g|=  1.00065D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.39911D+00    |proj g|=  1.48724D-01\n",
      "\n",
      "At iterate   20    f= -1.39942D+00    |proj g|=  2.64325D-01\n",
      "\n",
      "At iterate   25    f= -1.39952D+00    |proj g|=  1.60336D-01\n",
      "\n",
      "At iterate   30    f= -1.40139D+00    |proj g|=  5.72171D-01\n",
      "\n",
      "At iterate   35    f= -1.40181D+00    |proj g|=  2.48627D-01\n",
      "\n",
      "At iterate   40    f= -1.40334D+00    |proj g|=  4.96802D-01\n",
      "\n",
      "At iterate   45    f= -1.40396D+00    |proj g|=  5.58869D-02\n",
      "\n",
      "At iterate   50    f= -1.40444D+00    |proj g|=  1.52619D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     50     59      1     0     0   1.526D-02  -1.404D+00\n",
      "  F =  -1.4044409176062203     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34887D+00    |proj g|=  4.73164D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.36812D+00    |proj g|=  9.63867D-01\n",
      "\n",
      "At iterate   10    f= -1.38857D+00    |proj g|=  2.52078D-01\n",
      "\n",
      "At iterate   15    f= -1.39410D+00    |proj g|=  1.38153D+00\n",
      "\n",
      "At iterate   20    f= -1.40068D+00    |proj g|=  1.52900D+00\n",
      "\n",
      "At iterate   25    f= -1.40407D+00    |proj g|=  7.54177D-02\n",
      "\n",
      "At iterate   30    f= -1.40449D+00    |proj g|=  3.54108D-01\n",
      "\n",
      "At iterate   35    f= -1.40478D+00    |proj g|=  1.54213D-01\n",
      "\n",
      "At iterate   40    f= -1.40561D+00    |proj g|=  8.62983D-01\n",
      "\n",
      "At iterate   45    f= -1.40737D+00    |proj g|=  4.05448D-01\n",
      "\n",
      "At iterate   50    f= -1.40884D+00    |proj g|=  3.54892D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     62      1     0     0   3.549D-01  -1.409D+00\n",
      "  F =  -1.4088376999981675     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.10363D+00    |proj g|=  6.93857D+00\n",
      "\n",
      "At iterate    5    f= -1.58068D+00    |proj g|=  2.25711D+00\n",
      "\n",
      "At iterate   10    f= -1.64517D+00    |proj g|=  1.61292D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.64873D+00    |proj g|=  6.73705D-01\n",
      "\n",
      "At iterate   20    f= -1.65597D+00    |proj g|=  3.99439D-02\n",
      "\n",
      "At iterate   25    f= -1.65598D+00    |proj g|=  8.47525D-02\n",
      "\n",
      "At iterate   30    f= -1.65624D+00    |proj g|=  5.38247D-02\n",
      "\n",
      "At iterate   35    f= -1.65726D+00    |proj g|=  5.19199D-01\n",
      "\n",
      "At iterate   40    f= -1.65997D+00    |proj g|=  4.29896D-01\n",
      "\n",
      "At iterate   45    f= -1.66063D+00    |proj g|=  1.45599D-02\n",
      "\n",
      "At iterate   50    f= -1.66063D+00    |proj g|=  6.13783D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     50     79      1     0     0   6.138D-03  -1.661D+00\n",
      "  F =  -1.6606305234875052     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.17152D-01    |proj g|=  6.65368D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.41396D+00    |proj g|=  1.11152D+00\n",
      "\n",
      "At iterate   10    f= -1.46121D+00    |proj g|=  3.87617D-01\n",
      "\n",
      "At iterate   15    f= -1.47148D+00    |proj g|=  4.50901D-01\n",
      "\n",
      "At iterate   20    f= -1.47331D+00    |proj g|=  2.53353D-01\n",
      "\n",
      "At iterate   25    f= -1.48093D+00    |proj g|=  6.55854D-02\n",
      "\n",
      "At iterate   30    f= -1.48473D+00    |proj g|=  4.67171D-01\n",
      "\n",
      "At iterate   35    f= -1.49616D+00    |proj g|=  4.86769D-01\n",
      "\n",
      "At iterate   40    f= -1.49641D+00    |proj g|=  7.13162D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     40     59      1     0     0   7.132D-03  -1.496D+00\n",
      "  F =  -1.4964085957590787     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.47870D+00    |proj g|=  4.27611D+00\n",
      "\n",
      "At iterate    5    f= -1.50111D+00    |proj g|=  1.37850D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -1.50245D+00    |proj g|=  7.60590D-01\n",
      "\n",
      "At iterate   15    f= -1.50295D+00    |proj g|=  1.88944D-01\n",
      "\n",
      "At iterate   20    f= -1.50307D+00    |proj g|=  2.73055D-02\n",
      "\n",
      "At iterate   25    f= -1.50318D+00    |proj g|=  5.85857D-02\n",
      "\n",
      "At iterate   30    f= -1.50319D+00    |proj g|=  7.14085D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     31     47      1     0     0   1.108D-03  -1.503D+00\n",
      "  F =  -1.5031860855976040     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.33841D+00    |proj g|=  4.37916D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.36767D+00    |proj g|=  2.46171D+00\n",
      "\n",
      "At iterate   10    f= -1.39265D+00    |proj g|=  7.39714D-02\n",
      "\n",
      "At iterate   15    f= -1.39480D+00    |proj g|=  1.25762D+00\n",
      "\n",
      "At iterate   20    f= -1.39671D+00    |proj g|=  7.26165D-03\n",
      "\n",
      "At iterate   25    f= -1.39680D+00    |proj g|=  4.01082D-02\n",
      "\n",
      "At iterate   30    f= -1.39690D+00    |proj g|=  2.40680D-01\n",
      "\n",
      "At iterate   35    f= -1.39699D+00    |proj g|=  3.09474D-02\n",
      "\n",
      "At iterate   40    f= -1.39869D+00    |proj g|=  6.01553D-01\n",
      "\n",
      "At iterate   45    f= -1.40639D+00    |proj g|=  2.69435D-01\n",
      "\n",
      "At iterate   50    f= -1.40765D+00    |proj g|=  1.72444D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     68      1     0     0   1.724D-02  -1.408D+00\n",
      "  F =  -1.4076546588040584     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.01859D+00    |proj g|=  6.29497D+00\n",
      "\n",
      "At iterate    5    f= -1.38523D+00    |proj g|=  5.33550D+00\n",
      "\n",
      "At iterate   10    f= -1.46409D+00    |proj g|=  4.61661D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.52339D+00    |proj g|=  9.01167D-02\n",
      "\n",
      "At iterate   20    f= -1.52621D+00    |proj g|=  3.21962D-03\n",
      "\n",
      "At iterate   25    f= -1.52627D+00    |proj g|=  2.03536D-01\n",
      "\n",
      "At iterate   30    f= -1.52641D+00    |proj g|=  1.03619D-01\n",
      "\n",
      "At iterate   35    f= -1.52676D+00    |proj g|=  9.30944D-02\n",
      "\n",
      "At iterate   40    f= -1.52682D+00    |proj g|=  1.14288D-02\n",
      "\n",
      "At iterate   45    f= -1.52692D+00    |proj g|=  2.10909D-01\n",
      "\n",
      "At iterate   50    f= -1.53564D+00    |proj g|=  3.67966D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     74      1     0     0   3.680D-01  -1.536D+00\n",
      "  F =  -1.5356418575163227     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.25765D-01    |proj g|=  6.64005D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.40658D+00    |proj g|=  1.09599D+00\n",
      "\n",
      "At iterate   10    f= -1.46727D+00    |proj g|=  4.18640D-01\n",
      "\n",
      "At iterate   15    f= -1.48297D+00    |proj g|=  1.06274D+00\n",
      "\n",
      "At iterate   20    f= -1.48518D+00    |proj g|=  9.12744D-01\n",
      "\n",
      "At iterate   25    f= -1.51540D+00    |proj g|=  4.70829D+00\n",
      "\n",
      "At iterate   30    f= -1.53308D+00    |proj g|=  5.17810D-01\n",
      "\n",
      "At iterate   35    f= -1.53378D+00    |proj g|=  5.50665D-01\n",
      "\n",
      "At iterate   40    f= -1.54234D+00    |proj g|=  2.52297D+00\n",
      "\n",
      "At iterate   45    f= -1.54684D+00    |proj g|=  2.03963D-01\n",
      "\n",
      "At iterate   50    f= -1.54692D+00    |proj g|=  4.72276D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     68      1     0     0   4.723D-01  -1.547D+00\n",
      "  F =  -1.5469186870706129     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.37827D+00    |proj g|=  5.09274D+00\n",
      "\n",
      "At iterate    5    f= -1.39898D+00    |proj g|=  8.47746D-02\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -1.40151D+00    |proj g|=  1.46203D+00\n",
      "\n",
      "At iterate   15    f= -1.40310D+00    |proj g|=  2.85832D-01\n",
      "\n",
      "At iterate   20    f= -1.40397D+00    |proj g|=  3.39578D-01\n",
      "\n",
      "At iterate   25    f= -1.40405D+00    |proj g|=  2.93744D-02\n",
      "\n",
      "At iterate   30    f= -1.40408D+00    |proj g|=  9.59012D-03\n",
      "\n",
      "At iterate   35    f= -1.40410D+00    |proj g|=  1.19707D-01\n",
      "\n",
      "At iterate   40    f= -1.40419D+00    |proj g|=  3.66076D-02\n",
      "\n",
      "At iterate   45    f= -1.40435D+00    |proj g|=  5.91428D-02\n",
      "\n",
      "At iterate   50    f= -1.40436D+00    |proj g|=  2.64973D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     59      1     0     0   2.650D-03  -1.404D+00\n",
      "  F =  -1.4043600688896041     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.33710D+00    |proj g|=  4.60134D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.35626D+00    |proj g|=  1.31471D+00\n",
      "\n",
      "At iterate   10    f= -1.37426D+00    |proj g|=  7.70861D-02\n",
      "\n",
      "At iterate   15    f= -1.37560D+00    |proj g|=  1.41044D+00\n",
      "\n",
      "At iterate   20    f= -1.38396D+00    |proj g|=  3.30091D-01\n",
      "\n",
      "At iterate   25    f= -1.38853D+00    |proj g|=  1.26694D+00\n",
      "\n",
      "At iterate   30    f= -1.39183D+00    |proj g|=  7.06791D-02\n",
      "\n",
      "At iterate   35    f= -1.39281D+00    |proj g|=  4.26268D-01\n",
      "\n",
      "At iterate   40    f= -1.39391D+00    |proj g|=  7.95624D-02\n",
      "\n",
      "At iterate   45    f= -1.39470D+00    |proj g|=  1.86070D-02\n",
      "\n",
      "At iterate   50    f= -1.39499D+00    |proj g|=  1.65588D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     63      1     0     0   1.656D-02  -1.395D+00\n",
      "  F =  -1.3949873851279100     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.09979D+00    |proj g|=  6.55851D+00\n",
      "\n",
      "At iterate    5    f= -1.53550D+00    |proj g|=  4.01501D+00\n",
      "\n",
      "At iterate   10    f= -1.62310D+00    |proj g|=  1.62722D+00\n",
      "\n",
      "At iterate   15    f= -1.63977D+00    |proj g|=  6.65884D-01\n",
      "\n",
      "At iterate   20    f= -1.65993D+00    |proj g|=  1.52188D+00\n",
      "\n",
      "At iterate   25    f= -1.67701D+00    |proj g|=  1.60738D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   30    f= -1.67752D+00    |proj g|=  3.60326D-01\n",
      "\n",
      "At iterate   35    f= -1.68431D+00    |proj g|=  1.04556D+00\n",
      "\n",
      "At iterate   40    f= -1.69154D+00    |proj g|=  5.97189D-01\n",
      "\n",
      "At iterate   45    f= -1.69321D+00    |proj g|=  1.50811D-01\n",
      "\n",
      "At iterate   50    f= -1.69326D+00    |proj g|=  3.92519D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     50     68      1     0     0   3.925D-04  -1.693D+00\n",
      "  F =  -1.6932588099055508     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.89351D-01    |proj g|=  5.82062D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.38180D+00    |proj g|=  9.35958D-01\n",
      "\n",
      "At iterate   10    f= -1.43706D+00    |proj g|=  4.22069D-01\n",
      "\n",
      "At iterate   15    f= -1.46072D+00    |proj g|=  4.08693D-01\n",
      "\n",
      "At iterate   20    f= -1.48569D+00    |proj g|=  2.52103D+00\n",
      "\n",
      "At iterate   25    f= -1.51171D+00    |proj g|=  6.49152D-01\n",
      "\n",
      "At iterate   30    f= -1.51371D+00    |proj g|=  1.17023D-01\n",
      "\n",
      "At iterate   35    f= -1.51967D+00    |proj g|=  2.20026D-02\n",
      "\n",
      "At iterate   40    f= -1.52015D+00    |proj g|=  7.24012D-01\n",
      "\n",
      "At iterate   45    f= -1.52069D+00    |proj g|=  4.83209D-02\n",
      "\n",
      "At iterate   50    f= -1.52116D+00    |proj g|=  2.42107D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     64      1     0     0   2.421D-01  -1.521D+00\n",
      "  F =  -1.5211577246161057     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34297D+00    |proj g|=  2.49834D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.38368D+00    |proj g|=  1.93559D-01\n",
      "\n",
      "At iterate   10    f= -1.46213D+00    |proj g|=  2.55430D+00\n",
      "\n",
      "At iterate   15    f= -1.46838D+00    |proj g|=  2.83797D-01\n",
      "\n",
      "At iterate   20    f= -1.48405D+00    |proj g|=  2.45581D+00\n",
      "\n",
      "At iterate   25    f= -1.50420D+00    |proj g|=  3.66939D-01\n",
      "\n",
      "At iterate   30    f= -1.52678D+00    |proj g|=  3.53924D-02\n",
      "\n",
      "At iterate   35    f= -1.52821D+00    |proj g|=  5.96378D-01\n",
      "\n",
      "At iterate   40    f= -1.53398D+00    |proj g|=  5.31605D-02\n",
      "\n",
      "At iterate   45    f= -1.53404D+00    |proj g|=  1.14335D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     46     65      1     0     0   1.057D-03  -1.534D+00\n",
      "  F =  -1.5340361672626563     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34953D+00    |proj g|=  6.69997D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.38837D+00    |proj g|=  4.97898D-01\n",
      "\n",
      "At iterate   10    f= -1.39144D+00    |proj g|=  1.38910D-01\n",
      "\n",
      "At iterate   15    f= -1.39353D+00    |proj g|=  7.61705D-02\n",
      "\n",
      "At iterate   20    f= -1.39455D+00    |proj g|=  1.12140D-01\n",
      "\n",
      "At iterate   25    f= -1.40007D+00    |proj g|=  8.08367D-01\n",
      "\n",
      "At iterate   30    f= -1.41002D+00    |proj g|=  4.91562D-01\n",
      "\n",
      "At iterate   35    f= -1.41777D+00    |proj g|=  2.45065D+00\n",
      "\n",
      "At iterate   40    f= -1.42212D+00    |proj g|=  1.84296D-02\n",
      "\n",
      "At iterate   45    f= -1.42261D+00    |proj g|=  4.24004D-01\n",
      "\n",
      "At iterate   50    f= -1.43106D+00    |proj g|=  1.02687D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     66      1     0     0   1.027D+00  -1.431D+00\n",
      "  F =  -1.4310615975767080     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.00407D+00    |proj g|=  6.06278D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.43141D+00    |proj g|=  9.70884D-01\n",
      "\n",
      "At iterate   10    f= -1.49498D+00    |proj g|=  2.42012D-01\n",
      "\n",
      "At iterate   15    f= -1.50494D+00    |proj g|=  4.42822D-01\n",
      "\n",
      "At iterate   20    f= -1.50631D+00    |proj g|=  2.16357D-01\n",
      "\n",
      "At iterate   25    f= -1.51116D+00    |proj g|=  1.82617D+00\n",
      "\n",
      "At iterate   30    f= -1.52790D+00    |proj g|=  1.35497D-01\n",
      "\n",
      "At iterate   35    f= -1.52861D+00    |proj g|=  1.44288D-02\n",
      "\n",
      "At iterate   40    f= -1.52866D+00    |proj g|=  1.67815D-02\n",
      "\n",
      "At iterate   45    f= -1.52945D+00    |proj g|=  3.20114D-01\n",
      "\n",
      "At iterate   50    f= -1.53511D+00    |proj g|=  5.67455D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     68      1     0     0   5.675D-02  -1.535D+00\n",
      "  F =  -1.5351130617822915     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.93325D-01    |proj g|=  5.95969D+00\n",
      "\n",
      "At iterate    5    f= -1.36447D+00    |proj g|=  4.25209D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -1.46605D+00    |proj g|=  4.54317D-01\n",
      "\n",
      "At iterate   15    f= -1.52744D+00    |proj g|=  4.74238D-01\n",
      "\n",
      "At iterate   20    f= -1.52975D+00    |proj g|=  9.19344D-01\n",
      "\n",
      "At iterate   25    f= -1.53223D+00    |proj g|=  2.91427D-01\n",
      "\n",
      "At iterate   30    f= -1.53817D+00    |proj g|=  6.87079D-02\n",
      "\n",
      "At iterate   35    f= -1.53908D+00    |proj g|=  5.21629D-02\n",
      "\n",
      "At iterate   40    f= -1.54560D+00    |proj g|=  7.84481D-01\n",
      "\n",
      "At iterate   45    f= -1.55777D+00    |proj g|=  9.85674D-02\n",
      "\n",
      "At iterate   50    f= -1.55849D+00    |proj g|=  1.75600D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     63      1     0     0   1.756D-02  -1.558D+00\n",
      "  F =  -1.5584858914554585     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.22885D+00    |proj g|=  1.93210D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.28208D+00    |proj g|=  4.70258D-01\n",
      "\n",
      "At iterate   10    f= -1.33072D+00    |proj g|=  6.43024D+00\n",
      "\n",
      "At iterate   15    f= -1.38615D+00    |proj g|=  2.01445D-01\n",
      "\n",
      "At iterate   20    f= -1.39470D+00    |proj g|=  1.18895D+00\n",
      "\n",
      "At iterate   25    f= -1.39584D+00    |proj g|=  2.59233D-01\n",
      "\n",
      "At iterate   30    f= -1.39883D+00    |proj g|=  8.16677D-01\n",
      "\n",
      "At iterate   35    f= -1.40286D+00    |proj g|=  3.92589D-02\n",
      "\n",
      "At iterate   40    f= -1.40408D+00    |proj g|=  1.62368D-02\n",
      "\n",
      "At iterate   45    f= -1.40452D+00    |proj g|=  8.15882D-01\n",
      "\n",
      "At iterate   50    f= -1.40800D+00    |proj g|=  4.89559D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     61      1     0     0   4.896D-01  -1.408D+00\n",
      "  F =  -1.4079973085798363     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.28165D+00    |proj g|=  5.57757D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.30741D+00    |proj g|=  5.10777D-01\n",
      "\n",
      "At iterate   10    f= -1.32626D+00    |proj g|=  1.79312D+00\n",
      "\n",
      "At iterate   15    f= -1.33236D+00    |proj g|=  9.64362D-02\n",
      "\n",
      "At iterate   20    f= -1.35096D+00    |proj g|=  1.00489D+00\n",
      "\n",
      "At iterate   25    f= -1.39855D+00    |proj g|=  8.18261D-01\n",
      "\n",
      "At iterate   30    f= -1.39969D+00    |proj g|=  4.50310D-01\n",
      "\n",
      "At iterate   35    f= -1.40482D+00    |proj g|=  2.52914D+00\n",
      "\n",
      "At iterate   40    f= -1.40964D+00    |proj g|=  1.41260D-01\n",
      "\n",
      "At iterate   45    f= -1.40978D+00    |proj g|=  2.00531D-01\n",
      "\n",
      "At iterate   50    f= -1.41140D+00    |proj g|=  1.52545D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     58      1     0     0   1.525D+00  -1.411D+00\n",
      "  F =  -1.4113986270052374     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -5.31121D-01    |proj g|=  7.00002D+00\n",
      "\n",
      "At iterate    5    f= -8.60517D-01    |proj g|=  4.00262D+00\n",
      "\n",
      "At iterate   10    f= -9.08512D-01    |proj g|=  4.01661D-01\n",
      "\n",
      "At iterate   15    f= -1.03289D+00    |proj g|=  8.80535D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   20    f= -1.35809D+00    |proj g|=  8.51844D+00\n",
      "\n",
      "At iterate   25    f= -1.46594D+00    |proj g|=  2.95771D+00\n",
      "\n",
      "At iterate   30    f= -1.49023D+00    |proj g|=  4.09287D+00\n",
      "\n",
      "At iterate   35    f= -1.55850D+00    |proj g|=  1.25350D+01\n",
      "\n",
      "At iterate   40    f= -1.63361D+00    |proj g|=  5.33071D+00\n",
      "\n",
      "At iterate   45    f= -1.67713D+00    |proj g|=  8.97731D-01\n",
      "\n",
      "At iterate   50    f= -1.68049D+00    |proj g|=  8.47032D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     76      1     0     0   8.470D-01  -1.680D+00\n",
      "  F =  -1.6804917317902033     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -4.60173D-01    |proj g|=  5.95051D+00\n",
      "\n",
      "At iterate    5    f= -7.14512D-01    |proj g|=  4.72247D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -7.71699D-01    |proj g|=  8.01080D-01\n",
      "\n",
      "At iterate   15    f= -7.96685D-01    |proj g|=  3.54059D+00\n",
      "\n",
      "At iterate   20    f= -1.09810D+00    |proj g|=  1.16508D+01\n",
      "\n",
      "At iterate   25    f= -1.33338D+00    |proj g|=  7.60536D+00\n",
      "\n",
      "At iterate   30    f= -1.38617D+00    |proj g|=  4.12481D+00\n",
      "\n",
      "At iterate   35    f= -1.39711D+00    |proj g|=  1.03672D+00\n",
      "\n",
      "At iterate   40    f= -1.47929D+00    |proj g|=  1.87561D+00\n",
      "\n",
      "At iterate   45    f= -1.50726D+00    |proj g|=  1.14945D+00\n",
      "\n",
      "At iterate   50    f= -1.51023D+00    |proj g|=  4.57084D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     85      1     0     0   4.571D-01  -1.510D+00\n",
      "  F =  -1.5102323991140674     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.32981D+00    |proj g|=  2.81152D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.36858D+00    |proj g|=  1.98758D-01\n",
      "\n",
      "At iterate   10    f= -1.39327D+00    |proj g|=  4.67284D+00\n",
      "\n",
      "At iterate   15    f= -1.45782D+00    |proj g|=  2.02369D-01\n",
      "\n",
      "At iterate   20    f= -1.46042D+00    |proj g|=  2.29046D-01\n",
      "\n",
      "At iterate   25    f= -1.47182D+00    |proj g|=  7.32752D-01\n",
      "\n",
      "At iterate   30    f= -1.50007D+00    |proj g|=  1.61349D-01\n",
      "\n",
      "At iterate   35    f= -1.51850D+00    |proj g|=  6.43646D-01\n",
      "\n",
      "At iterate   40    f= -1.52155D+00    |proj g|=  1.32965D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     42     53      1     0     0   3.309D-03  -1.522D+00\n",
      "  F =  -1.5215501765568928     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34530D+00    |proj g|=  6.92627D+00\n",
      "\n",
      "At iterate    5    f= -1.38754D+00    |proj g|=  5.72541D-01\n",
      "\n",
      "At iterate   10    f= -1.38926D+00    |proj g|=  8.23797D-01\n",
      "\n",
      "At iterate   15    f= -1.39496D+00    |proj g|=  5.67109D-01\n",
      "\n",
      "At iterate   20    f= -1.39612D+00    |proj g|=  5.25124D-01\n",
      "\n",
      "At iterate   25    f= -1.40319D+00    |proj g|=  4.35281D-01\n",
      "\n",
      "At iterate   30    f= -1.40721D+00    |proj g|=  2.79840D+00\n",
      "\n",
      "At iterate   35    f= -1.40895D+00    |proj g|=  1.07719D+00\n",
      "\n",
      "At iterate   40    f= -1.41327D+00    |proj g|=  7.20240D-02\n",
      "\n",
      "At iterate   45    f= -1.41344D+00    |proj g|=  7.99709D-02\n",
      "\n",
      "At iterate   50    f= -1.41360D+00    |proj g|=  1.90238D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     64      1     0     0   1.902D-02  -1.414D+00\n",
      "  F =  -1.4136009879626246     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -4.81078D-01    |proj g|=  6.29540D+00\n",
      "\n",
      "At iterate    5    f= -7.88829D-01    |proj g|=  1.25192D+00\n",
      "\n",
      "At iterate   10    f= -8.12402D-01    |proj g|=  3.52256D+00\n",
      "\n",
      "At iterate   15    f= -1.04621D+00    |proj g|=  1.14061D+01\n",
      "\n",
      "At iterate   20    f= -1.22081D+00    |proj g|=  5.28475D+00\n",
      "\n",
      "At iterate   25    f= -1.24671D+00    |proj g|=  2.18844D+00\n",
      "\n",
      "At iterate   30    f= -1.51318D+00    |proj g|=  2.45656D+00\n",
      "\n",
      "At iterate   35    f= -1.51876D+00    |proj g|=  1.35346D-01\n",
      "\n",
      "At iterate   40    f= -1.52345D+00    |proj g|=  2.21664D+00\n",
      "\n",
      "At iterate   45    f= -1.52817D+00    |proj g|=  9.81466D-02\n",
      "\n",
      "At iterate   50    f= -1.52847D+00    |proj g|=  1.99359D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     82      1     0     0   1.994D-01  -1.528D+00\n",
      "  F =  -1.5284716760738883     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            9     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -4.61380D-01    |proj g|=  6.04376D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -7.44580D-01    |proj g|=  2.25063D+00\n",
      "\n",
      "At iterate   10    f= -7.91843D-01    |proj g|=  7.08158D-01\n",
      "\n",
      "At iterate   15    f= -9.77643D-01    |proj g|=  2.05981D+00\n",
      "\n",
      "At iterate   20    f= -1.04829D+00    |proj g|=  1.53539D+00\n",
      "\n",
      "At iterate   25    f= -1.13083D+00    |proj g|=  2.33709D+00\n",
      "\n",
      "At iterate   30    f= -1.20693D+00    |proj g|=  2.45740D+00\n",
      "\n",
      "At iterate   35    f= -1.37868D+00    |proj g|=  2.92599D+00\n",
      "\n",
      "At iterate   40    f= -1.52917D+00    |proj g|=  5.14773D-01\n",
      "\n",
      "At iterate   45    f= -1.53158D+00    |proj g|=  1.16270D+00\n",
      "\n",
      "At iterate   50    f= -1.54583D+00    |proj g|=  7.39844D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    9     50     75      1     0     0   7.398D-01  -1.546D+00\n",
      "  F =  -1.5458263381788699     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.22792D+00    |proj g|=  2.15930D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.27624D+00    |proj g|=  1.78467D-01\n",
      "\n",
      "At iterate   10    f= -1.33512D+00    |proj g|=  6.05096D+00\n",
      "\n",
      "At iterate   15    f= -1.38213D+00    |proj g|=  2.37238D-01\n",
      "\n",
      "At iterate   20    f= -1.40328D+00    |proj g|=  9.09346D-01\n",
      "\n",
      "At iterate   25    f= -1.40484D+00    |proj g|=  2.38394D-01\n",
      "\n",
      "At iterate   30    f= -1.40751D+00    |proj g|=  6.32495D-02\n",
      "\n",
      "At iterate   35    f= -1.40781D+00    |proj g|=  2.25615D-02\n",
      "\n",
      "At iterate   40    f= -1.40824D+00    |proj g|=  5.18510D-02\n",
      "\n",
      "At iterate   45    f= -1.40824D+00    |proj g|=  8.06135D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     45     61      1     0     0   8.061D-04  -1.408D+00\n",
      "  F =  -1.4082428938041016     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            9     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.28275D+00    |proj g|=  5.86069D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.31220D+00    |proj g|=  1.07340D+00\n",
      "\n",
      "At iterate   10    f= -1.33638D+00    |proj g|=  4.36280D-01\n",
      "\n",
      "At iterate   15    f= -1.34851D+00    |proj g|=  3.91985D+00\n",
      "\n",
      "At iterate   20    f= -1.36866D+00    |proj g|=  7.65045D-01\n",
      "\n",
      "At iterate   25    f= -1.37850D+00    |proj g|=  8.48235D-01\n",
      "\n",
      "At iterate   30    f= -1.40179D+00    |proj g|=  1.75467D-01\n",
      "\n",
      "At iterate   35    f= -1.40239D+00    |proj g|=  1.17995D-01\n",
      "\n",
      "At iterate   40    f= -1.40822D+00    |proj g|=  7.62162D-01\n",
      "\n",
      "At iterate   45    f= -1.41291D+00    |proj g|=  1.35592D-01\n",
      "\n",
      "At iterate   50    f= -1.41297D+00    |proj g|=  2.44278D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    9     50     63      1     0     0   2.443D-02  -1.413D+00\n",
      "  F =  -1.4129661013518586     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.11375D+00    |proj g|=  7.03718D+00\n",
      "\n",
      "At iterate    5    f= -1.61293D+00    |proj g|=  2.06361D+00\n",
      "\n",
      "At iterate   10    f= -1.66405D+00    |proj g|=  2.35686D+00\n",
      "\n",
      "At iterate   15    f= -1.67426D+00    |proj g|=  2.56037D-01\n",
      "\n",
      "At iterate   20    f= -1.67609D+00    |proj g|=  7.59659D-03\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   25    f= -1.67629D+00    |proj g|=  8.67057D-02\n",
      "\n",
      "At iterate   30    f= -1.67654D+00    |proj g|=  9.35468D-03\n",
      "\n",
      "At iterate   35    f= -1.67654D+00    |proj g|=  1.02889D-02\n",
      "\n",
      "At iterate   40    f= -1.67654D+00    |proj g|=  2.90651D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     40     60      1     0     0   2.907D-03  -1.677D+00\n",
      "  F =  -1.6765421098293796     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.23653D-01    |proj g|=  6.66390D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.42343D+00    |proj g|=  8.31211D-01\n",
      "\n",
      "At iterate   10    f= -1.45586D+00    |proj g|=  2.02543D+00\n",
      "\n",
      "At iterate   15    f= -1.48646D+00    |proj g|=  4.59616D-01\n",
      "\n",
      "At iterate   20    f= -1.49342D+00    |proj g|=  1.45645D-01\n",
      "\n",
      "At iterate   25    f= -1.49561D+00    |proj g|=  7.10210D-01\n",
      "\n",
      "At iterate   30    f= -1.50830D+00    |proj g|=  8.91668D-02\n",
      "\n",
      "At iterate   35    f= -1.51247D+00    |proj g|=  2.56835D-01\n",
      "\n",
      "At iterate   40    f= -1.51265D+00    |proj g|=  8.08730D-02\n",
      "\n",
      "At iterate   45    f= -1.51266D+00    |proj g|=  2.93673D-02\n",
      "\n",
      "At iterate   50    f= -1.51268D+00    |proj g|=  4.33182D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     70      1     0     0   4.332D-02  -1.513D+00\n",
      "  F =  -1.5126790992851726     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            6     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  6.21675D-01    |proj g|=  3.74459D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f=  6.12220D-01    |proj g|=  5.25824D-01\n",
      "\n",
      "At iterate   10    f=  5.99508D-01    |proj g|=  1.72214D+00\n",
      "\n",
      "At iterate   15    f= -2.85904D-02    |proj g|=  2.82907D+01\n",
      "\n",
      "At iterate   20    f= -3.43848D-01    |proj g|=  1.64501D+00\n",
      "\n",
      "At iterate   25    f= -3.70018D-01    |proj g|=  9.38869D+00\n",
      "\n",
      "At iterate   30    f= -1.01552D+00    |proj g|=  5.67874D+01\n",
      "\n",
      "At iterate   35    f= -1.50942D+00    |proj g|=  1.19574D+01\n",
      "\n",
      "At iterate   40    f= -1.50992D+00    |proj g|=  8.46807D-01\n",
      "\n",
      "At iterate   45    f= -1.50993D+00    |proj g|=  1.49770D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    6     45     81      1     0     0   1.498D-01  -1.510D+00\n",
      "  F =  -1.5099261994601398     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  5.52747D-01    |proj g|=  3.54717D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f=  5.42804D-01    |proj g|=  6.59438D-01\n",
      "\n",
      "At iterate   10    f=  5.10245D-01    |proj g|=  3.66490D+00\n",
      "\n",
      "At iterate   15    f=  5.01241D-01    |proj g|=  3.92070D-01\n",
      "\n",
      "At iterate   20    f=  4.96070D-01    |proj g|=  3.51705D-01\n",
      "\n",
      "At iterate   25    f=  3.77305D-01    |proj g|=  9.20002D+00\n",
      "\n",
      "At iterate   30    f= -3.87815D-01    |proj g|=  2.68270D+00\n",
      "\n",
      "At iterate   35    f= -5.23846D-01    |proj g|=  5.56324D+01\n",
      "\n",
      "At iterate   40    f= -6.55054D-01    |proj g|=  5.63889D+01\n",
      "\n",
      "At iterate   45    f= -7.25582D-01    |proj g|=  1.27500D+02\n",
      "\n",
      "At iterate   50    f= -1.16047D+00    |proj g|=  7.89337D+02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     80      1     0     0   7.893D+02  -1.160D+00\n",
      "  F =  -1.1604730202253657     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.01185D+00    |proj g|=  6.27438D+00\n",
      "\n",
      "At iterate    5    f= -1.36655D+00    |proj g|=  4.07098D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   10    f= -1.41620D+00    |proj g|=  3.78510D+00\n",
      "\n",
      "At iterate   15    f= -1.47515D+00    |proj g|=  2.43969D-01\n",
      "\n",
      "At iterate   20    f= -1.51129D+00    |proj g|=  2.12825D+00\n",
      "\n",
      "At iterate   25    f= -1.51687D+00    |proj g|=  9.74287D-02\n",
      "\n",
      "At iterate   30    f= -1.51716D+00    |proj g|=  7.90460D-03\n",
      "\n",
      "At iterate   35    f= -1.51723D+00    |proj g|=  2.47119D-01\n",
      "\n",
      "At iterate   40    f= -1.51809D+00    |proj g|=  2.76202D-02\n",
      "\n",
      "At iterate   45    f= -1.51830D+00    |proj g|=  3.29165D-02\n",
      "\n",
      "At iterate   50    f= -1.51830D+00    |proj g|=  3.14949D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     50     62      1     0     0   3.149D-03  -1.518D+00\n",
      "  F =  -1.5183020457863217     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.32324D-01    |proj g|=  6.69103D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.39758D+00    |proj g|=  9.34906D-01\n",
      "\n",
      "At iterate   10    f= -1.47152D+00    |proj g|=  1.32019D+00\n",
      "\n",
      "At iterate   15    f= -1.48844D+00    |proj g|=  9.27101D-02\n",
      "\n",
      "At iterate   20    f= -1.49253D+00    |proj g|=  1.64157D+00\n",
      "\n",
      "At iterate   25    f= -1.50629D+00    |proj g|=  3.09866D-01\n",
      "\n",
      "At iterate   30    f= -1.52671D+00    |proj g|=  3.67666D+00\n",
      "\n",
      "At iterate   35    f= -1.54211D+00    |proj g|=  1.32792D+00\n",
      "\n",
      "At iterate   40    f= -1.54239D+00    |proj g|=  4.00831D-01\n",
      "\n",
      "At iterate   45    f= -1.54330D+00    |proj g|=  3.33159D-01\n",
      "\n",
      "At iterate   50    f= -1.54341D+00    |proj g|=  2.57837D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     65      1     0     0   2.578D-02  -1.543D+00\n",
      "  F =  -1.5434052456362028     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  5.29614D-01    |proj g|=  4.60607D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f=  5.13429D-01    |proj g|=  9.46531D-02\n",
      "\n",
      "At iterate   10    f=  5.11269D-01    |proj g|=  1.42920D+00\n",
      "\n",
      "At iterate   15    f=  5.08743D-01    |proj g|=  6.52370D-01\n",
      "\n",
      "At iterate   20    f=  4.78707D-01    |proj g|=  5.38184D+00\n",
      "\n",
      "At iterate   25    f=  3.10264D-01    |proj g|=  2.98708D+00\n",
      "\n",
      "At iterate   30    f= -4.44746D-01    |proj g|=  7.02340D+00\n",
      "\n",
      "At iterate   35    f= -6.48475D-01    |proj g|=  5.18794D+01\n",
      "\n",
      "At iterate   40    f= -6.88342D-01    |proj g|=  2.00477D+00\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     42     77      1     0     0   4.934D-01  -6.884D-01\n",
      "  F = -0.68838653883367773     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f=  5.53877D-01    |proj g|=  3.81279D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f=  5.42216D-01    |proj g|=  7.67417D-01\n",
      "\n",
      "At iterate   10    f=  5.19846D-01    |proj g|=  5.61121D-01\n",
      "\n",
      "At iterate   15    f=  5.07019D-01    |proj g|=  1.04304D+00\n",
      "\n",
      "At iterate   20    f=  4.90756D-01    |proj g|=  2.72404D-01\n",
      "\n",
      "At iterate   25    f=  4.72465D-01    |proj g|=  5.78370D-02\n",
      "\n",
      "At iterate   30    f=  4.69408D-01    |proj g|=  1.48156D+00\n",
      "\n",
      "At iterate   35    f=  3.87619D-01    |proj g|=  1.47153D+01\n",
      "  ys=-4.989E-03  -gs= 2.028E-03 BFGS update SKIPPED\n",
      "\n",
      "At iterate   40    f=  3.77390D-01    |proj g|=  7.74152D-01\n",
      "\n",
      "At iterate   45    f=  3.69137D-01    |proj g|=  3.84945D+00\n",
      "\n",
      "At iterate   50    f=  1.08368D-01    |proj g|=  1.13301D+02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     98      1     1     0   1.133D+02   1.084D-01\n",
      "  F =  0.10836799136334724     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.10347D+00    |proj g|=  6.93642D+00\n",
      "\n",
      "At iterate    5    f= -1.60226D+00    |proj g|=  2.70958D+00\n",
      "\n",
      "At iterate   10    f= -1.66187D+00    |proj g|=  6.26540D-01\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate   15    f= -1.66225D+00    |proj g|=  2.58478D-01\n",
      "\n",
      "At iterate   20    f= -1.66280D+00    |proj g|=  2.71953D-02\n",
      "\n",
      "At iterate   25    f= -1.66318D+00    |proj g|=  3.89346D-01\n",
      "\n",
      "At iterate   30    f= -1.66374D+00    |proj g|=  2.02174D-01\n",
      "\n",
      "At iterate   35    f= -1.66453D+00    |proj g|=  3.29665D-02\n",
      "\n",
      "At iterate   40    f= -1.66528D+00    |proj g|=  3.13480D-02\n",
      "\n",
      "At iterate   45    f= -1.66532D+00    |proj g|=  4.95069D-04\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     48     79      1     0     0   9.296D-04  -1.665D+00\n",
      "  F =  -1.6653158498588294     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.17092D-01    |proj g|=  6.65338D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " Warning:  more than 10 function and gradient\n",
      "   evaluations in the last line search.  Termination\n",
      "   may possibly be caused by a bad search direction.\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.42107D+00    |proj g|=  1.13930D+00\n",
      "\n",
      "At iterate   10    f= -1.46957D+00    |proj g|=  8.44535D-01\n",
      "\n",
      "At iterate   15    f= -1.48094D+00    |proj g|=  4.71356D-02\n",
      "\n",
      "At iterate   20    f= -1.48160D+00    |proj g|=  5.98122D-01\n",
      "\n",
      "At iterate   25    f= -1.48697D+00    |proj g|=  1.05609D-01\n",
      "\n",
      "At iterate   30    f= -1.49459D+00    |proj g|=  2.23619D-01\n",
      "\n",
      "At iterate   35    f= -1.49691D+00    |proj g|=  2.30897D-02\n",
      "\n",
      "At iterate   40    f= -1.49692D+00    |proj g|=  1.22206D-01\n",
      "\n",
      "At iterate   45    f= -1.49693D+00    |proj g|=  2.99012D-02\n",
      "\n",
      "At iterate   50    f= -1.49696D+00    |proj g|=  7.60275D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     67      1     0     0   7.603D-03  -1.497D+00\n",
      "  F =  -1.4969649130435119     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            7     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.48578D+00    |proj g|=  4.33050D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.50539D+00    |proj g|=  1.14429D+00\n",
      "\n",
      "At iterate   10    f= -1.50681D+00    |proj g|=  3.09805D-01\n",
      "\n",
      "At iterate   15    f= -1.50827D+00    |proj g|=  3.36713D-01\n",
      "\n",
      "At iterate   20    f= -1.50891D+00    |proj g|=  2.76336D-01\n",
      "\n",
      "At iterate   25    f= -1.50916D+00    |proj g|=  2.84958D-02\n",
      "\n",
      "At iterate   30    f= -1.50917D+00    |proj g|=  2.54726D-03\n",
      "\n",
      "At iterate   35    f= -1.50921D+00    |proj g|=  2.04884D-02\n",
      "\n",
      "At iterate   40    f= -1.51108D+00    |proj g|=  3.33381D-01\n",
      "\n",
      "At iterate   45    f= -1.51313D+00    |proj g|=  1.35666D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    7     49     73      1     0     0   6.464D-04  -1.513D+00\n",
      "  F =  -1.5131343669384179     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.34158D+00    |proj g|=  4.24067D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.35829D+00    |proj g|=  1.30024D+00\n",
      "\n",
      "At iterate   10    f= -1.40191D+00    |proj g|=  7.90523D-01\n",
      "\n",
      "At iterate   15    f= -1.40332D+00    |proj g|=  6.68830D-02\n",
      "\n",
      "At iterate   20    f= -1.40408D+00    |proj g|=  2.46320D-02\n",
      "\n",
      "At iterate   25    f= -1.40557D+00    |proj g|=  6.98498D-02\n",
      "\n",
      "At iterate   30    f= -1.40758D+00    |proj g|=  1.11964D-01\n",
      "\n",
      "At iterate   35    f= -1.40920D+00    |proj g|=  5.67226D-02\n",
      "\n",
      "At iterate   40    f= -1.40966D+00    |proj g|=  1.46116D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     43     66      1     0     0   8.676D-03  -1.410D+00\n",
      "  F =  -1.4096608041743715     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.01124D+00    |proj g|=  6.26798D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.38549D+00    |proj g|=  5.03603D+00\n",
      "\n",
      "At iterate   10    f= -1.50550D+00    |proj g|=  3.60576D+00\n",
      "\n",
      "At iterate   15    f= -1.51996D+00    |proj g|=  9.68217D-02\n",
      "\n",
      "At iterate   20    f= -1.52038D+00    |proj g|=  1.95184D-01\n",
      "\n",
      "At iterate   25    f= -1.52106D+00    |proj g|=  1.05367D-01\n",
      "\n",
      "At iterate   30    f= -1.52779D+00    |proj g|=  1.87057D+00\n",
      "\n",
      "At iterate   35    f= -1.53157D+00    |proj g|=  1.33294D-01\n",
      "\n",
      "At iterate   40    f= -1.53168D+00    |proj g|=  2.16206D-01\n",
      "\n",
      "At iterate   45    f= -1.53380D+00    |proj g|=  1.04164D+00\n",
      "\n",
      "At iterate   50    f= -1.53575D+00    |proj g|=  2.85970D-01\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     62      1     0     0   2.860D-01  -1.536D+00\n",
      "  F =  -1.5357533111740882     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n",
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            9     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -9.25730D-01    |proj g|=  6.64022D+00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "At iterate    5    f= -1.41397D+00    |proj g|=  1.12710D+00\n",
      "\n",
      "At iterate   10    f= -1.47308D+00    |proj g|=  1.12582D-01\n",
      "\n",
      "At iterate   15    f= -1.49180D+00    |proj g|=  8.29546D-01\n",
      "\n",
      "At iterate   20    f= -1.49465D+00    |proj g|=  7.47167D-01\n",
      "\n",
      "At iterate   25    f= -1.52734D+00    |proj g|=  2.12126D-01\n",
      "\n",
      "At iterate   30    f= -1.53063D+00    |proj g|=  1.78754D-01\n",
      "\n",
      "At iterate   35    f= -1.53222D+00    |proj g|=  1.04191D+00\n",
      "\n",
      "At iterate   40    f= -1.53300D+00    |proj g|=  1.29091D-01\n",
      "\n",
      "At iterate   45    f= -1.53788D+00    |proj g|=  2.96571D+00\n",
      "\n",
      "At iterate   50    f= -1.53940D+00    |proj g|=  6.48124D-02\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    9     50     63      1     0     0   6.481D-02  -1.539D+00\n",
      "  F =  -1.5394048761582821     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            8     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.37659D+00    |proj g|=  5.25448D+00\n",
      "\n",
      "At iterate    5    f= -1.39863D+00    |proj g|=  2.16848D-01\n",
      "\n",
      "At iterate   10    f= -1.40472D+00    |proj g|=  1.86123D+00\n",
      "\n",
      "At iterate   15    f= -1.40646D+00    |proj g|=  5.47204D-02\n",
      "\n",
      "At iterate   20    f= -1.40862D+00    |proj g|=  1.92215D-01\n",
      "\n",
      "At iterate   25    f= -1.40898D+00    |proj g|=  1.69356D-02\n",
      "\n",
      "At iterate   30    f= -1.40989D+00    |proj g|=  4.47314D-02\n",
      "\n",
      "At iterate   35    f= -1.41060D+00    |proj g|=  1.76302D-02\n",
      "\n",
      "At iterate   40    f= -1.41115D+00    |proj g|=  1.91191D-02\n",
      "\n",
      "At iterate   45    f= -1.41119D+00    |proj g|=  1.28722D-01\n",
      "\n",
      "At iterate   50    f= -1.41123D+00    |proj g|=  9.66471D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    8     50     59      1     0     0   9.665D-03  -1.411D+00\n",
      "  F =  -1.4112294321622363     \n",
      "\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            9     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -1.33830D+00    |proj g|=  4.40677D+00\n",
      "\n",
      "At iterate    5    f= -1.35646D+00    |proj g|=  1.51929D+00\n",
      "\n",
      "At iterate   10    f= -1.37913D+00    |proj g|=  9.66140D-01\n",
      "\n",
      "At iterate   15    f= -1.38547D+00    |proj g|=  3.12866D-01\n",
      "\n",
      "At iterate   20    f= -1.39231D+00    |proj g|=  6.37296D-02\n",
      "\n",
      "At iterate   25    f= -1.39526D+00    |proj g|=  8.96308D-01\n",
      "\n",
      "At iterate   30    f= -1.39823D+00    |proj g|=  2.30819D-02\n",
      "\n",
      "At iterate   35    f= -1.39901D+00    |proj g|=  9.79988D-02\n",
      "  ys=-1.576E-02  -gs= 1.067E-04 BFGS update SKIPPED\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")           #Specify to ignore warning messages\n",
    "AIC_df = pd.DataFrame({}, columns = ['param', 'param_seasonal', 'AIC'])\n",
    "for param in pdq:\n",
    "    for param_seasonal in seasonal_pdq:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(ts_log, order = param, seasonal_order = param_seasonal, \n",
    "            enforce_stationarity = False, enforce_invertibility = False)\n",
    "            results = mod.fit()\n",
    "            temp = pd.DataFrame([[param, param_seasonal, results.aic]], columns = ['param', 'param_seasonal', 'AIC'])\n",
    "            AIC_df = AIC_df.append(temp, ignore_index = True)\n",
    "            del temp\n",
    "        except:\n",
    "            continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "k_RETUZ3wk76",
    "outputId": "44948c3c-60a9-45c5-fa08-c29b86aa13b1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>param_seasonal</th>\n",
       "      <th>AIC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(1, 0, 1)</td>\n",
       "      <td>(1, 0, 1, 12)</td>\n",
       "      <td>-477.619383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(1, 0, 1)</td>\n",
       "      <td>(1, 0, 2, 12)</td>\n",
       "      <td>-428.989465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(1, 0, 1)</td>\n",
       "      <td>(1, 1, 1, 12)</td>\n",
       "      <td>-431.572183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(1, 0, 1)</td>\n",
       "      <td>(1, 1, 2, 12)</td>\n",
       "      <td>-398.431031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(1, 0, 1)</td>\n",
       "      <td>(2, 0, 1, 12)</td>\n",
       "      <td>-434.693439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       param param_seasonal         AIC\n",
       "0  (1, 0, 1)  (1, 0, 1, 12) -477.619383\n",
       "1  (1, 0, 1)  (1, 0, 2, 12) -428.989465\n",
       "2  (1, 0, 1)  (1, 1, 1, 12) -431.572183\n",
       "3  (1, 0, 1)  (1, 1, 2, 12) -398.431031\n",
       "4  (1, 0, 1)  (2, 0, 1, 12) -434.693439"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AIC_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gD7O_Sp8vtpn",
    "outputId": "b98967a8-9dc0-409e-aa6f-36f239ca5bab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Statespace Model Results                                 \n",
      "==========================================================================================\n",
      "Dep. Variable:                        #Passengers   No. Observations:                  144\n",
      "Model:             SARIMAX(1, 0, 1)x(1, 0, 1, 12)   Log Likelihood                 243.810\n",
      "Date:                            Mon, 08 Feb 2021   AIC                           -477.619\n",
      "Time:                                    14:08:57   BIC                           -463.282\n",
      "Sample:                                01-01-1949   HQIC                          -471.794\n",
      "                                     - 12-01-1960                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.9419      0.039     24.099      0.000       0.865       1.019\n",
      "ma.L1         -0.3803      0.093     -4.092      0.000      -0.562      -0.198\n",
      "ar.S.L12       1.0200      0.003    302.457      0.000       1.013       1.027\n",
      "ma.S.L12      -0.5747      0.107     -5.387      0.000      -0.784      -0.366\n",
      "sigma2         0.0013      0.000      9.240      0.000       0.001       0.002\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       34.14   Jarque-Bera (JB):                 7.00\n",
      "Prob(Q):                              0.73   Prob(JB):                         0.03\n",
      "Heteroskedasticity (H):               0.56   Skew:                            -0.01\n",
      "Prob(H) (two-sided):                  0.06   Kurtosis:                         4.14\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "#To get the best results based on the lowest AIC score\n",
    "min_aic = AIC_df.sort_values(by = 'AIC').iloc[0]    #Row with minimum AIC value\n",
    "model = sm.tsa.statespace.SARIMAX(ts_log, order = min_aic.param,seasonal_order = min_aic.param_seasonal, \n",
    "enforce_stationarity = False, enforce_invertibility = False)\n",
    "results = model.fit()\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 463
    },
    "id": "cgLeW4T51QNf",
    "outputId": "eda45ce3-5415-4b83-c7ae-eb3be3f6e15c"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot_diagnostics(figsize=(18, 8))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FUvizGqsw7N0"
   },
   "source": [
    "*Model diagnostics suggests that the residuals of our model aren't correlated and are normally distributed with 0 mean. The qq-plot shows that the ordered distribution of residuals (blue dots) follows the linear trend of the samples taken from a standard normal distribution with N(0, 1) and the correlogram depicts that the time series residuals have low correlation with lagged versions of itself.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nTIjYg7b-Zzk"
   },
   "source": [
    "# Steps for ARIMA implementation\n",
    "**The general steps to implement an ARIMA model are –**\n",
    "\n",
    "1) **Load the data**: *The first step for model building is of course to load the dataset.*\n",
    "<br>\n",
    "2) **Preprocessing**: *Depending on the dataset, the steps of preprocessing will be defined. This will include creating timestamps, converting the dtype of date/time column, making the series univariate, etc.* \n",
    "<br>\n",
    "3) **Make series stationary**: *In order to satisfy the assumption, it is necessary to make the series stationary. This would include checking the stationarity of the series and performing required transformations*\n",
    "<br>\n",
    "4) **Determine d value**: *For making the series stationary, the number of times the difference operation was performed will be taken as the d value*\n",
    "<br>\n",
    "5) **Create ACF and PACF plots**: *This is the most important step in ARIMA implementation. ACF PACF plots are used to determine the input parameters for our ARIMA model*\n",
    "<br>\n",
    "6)**Determine the p and q values**: *Read the values of p and q from the plots in the previous step.*\n",
    "<br>\n",
    "7) **Fit ARIMA model**: *Using the processed data and parameter values we calculated from the previous steps, fit the ARIMA model*\n",
    "<br>\n",
    "8) **Predict values on validation set**: *Predict the future values*\n",
    "<br>\n",
    "9) **Calculate RMSE**: *To check the performance of the model, check the RMSE value using the predictions and actual values on the validation set. *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "x8-QkGmKLvif",
    "outputId": "401bc43a-b5b6-4ff0-897c-7c53a744f2a0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>#Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1949-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1949-02</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1949-03</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1949-04</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1949-05</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  #Passengers\n",
       "0  1949-01          112\n",
       "1  1949-02          118\n",
       "2  1949-03          132\n",
       "3  1949-04          129\n",
       "4  1949-05          121"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('AirPassengers.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wX3DVUoJBKDy"
   },
   "source": [
    "## What Auto ARIMA does?\n",
    "*Before implementing ARIMA, the series has to be made stationary,and determine the values of p and q using the plots we discussed above. Auto ARIMA makes this task really simple for us as it eliminates steps 3 to 6 displayed in the ARIMA section.* "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HiCbRUIdAHbT"
   },
   "source": [
    "# Steps for Auto-ARIMA implementation\n",
    "**The general steps to implement an Auto-ARIMA model are –**\n",
    "\n",
    "1) **Load the data**: *The first step for model building is of course to load the dataset.*\n",
    "<br>\n",
    "2) **Preprocessing**: *Depending on the dataset, the steps of preprocessing will be defined. This will include creating timestamps, converting the dtype of date/time column, making the series univariate, etc.* \n",
    "<br>\n",
    "3) **Fit Auto ARIMA**: *Using the processed data and parameter values we calculated from the previous steps, fit the Auto-ARIMA model*\n",
    "<br>\n",
    "4) **Predict values on validation set**: *Predict the future values*\n",
    "<br>\n",
    "5) **Calculate RMSE**: *To check the performance of the model, check the RMSE value using the predictions and actual values on the validation set.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 818
    },
    "id": "2Gqq6_3Ne6vN",
    "outputId": "fa5447a2-e9aa-4164-bd0c-957b7b56f81f"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pmdarima'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[0;32m/var/folders/x5/46xxxny51s7dgh2f4k1_ys280000gn/T/ipykernel_63863/2553195207.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m# Import the library\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpmdarima\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mauto_arima\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0;31m# Ignore harmless warnings\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mwarnings\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'pmdarima'"
     ]
    }
   ],
   "source": [
    "\n",
    "  \n",
    "# Import the library \n",
    "from pmdarima import auto_arima \n",
    "  \n",
    "# Ignore harmless warnings \n",
    "import warnings \n",
    "warnings.filterwarnings(\"ignore\") \n",
    "  \n",
    "# Fit auto_arima function to AirPassengers dataset \n",
    "stepwise_fit = auto_arima(ts, start_p = 0, start_q = 0, \n",
    "                          max_p = 3, max_q = 3, m = 12, \n",
    "                          start_P = 0, seasonal = True, \n",
    "                          d = None, D = 1, trace = True, \n",
    "                          error_action ='ignore',   # we don't want to know if an order does not work \n",
    "                          suppress_warnings = True,  # we don't want convergence warnings \n",
    "                          stepwise = True)           # set to stepwise \n",
    "  \n",
    "# To print the summary \n",
    "stepwise_fit.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 444
    },
    "id": "MTrFyv1tLbqP",
    "outputId": "7b0c8dca-2bf1-4588-8f14-4291bcc0af46"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>             <td>#Passengers</td>          <th>  No. Observations:  </th>    <td>132</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(0, 1, 1)x(2, 1, 0, 12)</td> <th>  Log Likelihood     </th> <td>-446.830</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>                   <td>Mon, 08 Feb 2021</td>        <th>  AIC                </th>  <td>901.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                       <td>14:27:08</td>            <th>  BIC                </th>  <td>912.776</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>                         <td>0</td>               <th>  HQIC               </th>  <td>906.173</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                             <td> - 132</td>             <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>    <td>   -0.2556</td> <td>    0.080</td> <td>   -3.190</td> <td> 0.001</td> <td>   -0.413</td> <td>   -0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L12</th> <td>   -0.0962</td> <td>    0.090</td> <td>   -1.073</td> <td> 0.283</td> <td>   -0.272</td> <td>    0.080</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.S.L24</th> <td>    0.1454</td> <td>    0.101</td> <td>    1.445</td> <td> 0.149</td> <td>   -0.052</td> <td>    0.343</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th>   <td>  106.2408</td> <td>   15.243</td> <td>    6.970</td> <td> 0.000</td> <td>   76.364</td> <td>  136.117</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>42.14</td> <th>  Jarque-Bera (JB):  </th> <td>0.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.38</td>  <th>  Prob(JB):          </th> <td>0.99</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>1.63</td>  <th>  Skew:              </th> <td>0.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.12</td>  <th>  Kurtosis:          </th> <td>2.95</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 Statespace Model Results                                 \n",
       "==========================================================================================\n",
       "Dep. Variable:                        #Passengers   No. Observations:                  132\n",
       "Model:             SARIMAX(0, 1, 1)x(2, 1, 0, 12)   Log Likelihood                -446.830\n",
       "Date:                            Mon, 08 Feb 2021   AIC                            901.659\n",
       "Time:                                    14:27:08   BIC                            912.776\n",
       "Sample:                                         0   HQIC                           906.173\n",
       "                                            - 132                                         \n",
       "Covariance Type:                              opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.2556      0.080     -3.190      0.001      -0.413      -0.099\n",
       "ar.S.L12      -0.0962      0.090     -1.073      0.283      -0.272       0.080\n",
       "ar.S.L24       0.1454      0.101      1.445      0.149      -0.052       0.343\n",
       "sigma2       106.2408     15.243      6.970      0.000      76.364     136.117\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       42.14   Jarque-Bera (JB):                 0.01\n",
       "Prob(Q):                              0.38   Prob(JB):                         0.99\n",
       "Heteroskedasticity (H):               1.63   Skew:                             0.01\n",
       "Prob(H) (two-sided):                  0.12   Kurtosis:                         2.95\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split data into train / test sets \n",
    "train = df.iloc[:len(df)-12] \n",
    "test = df.iloc[len(df)-12:] # set one year(12 months) for testing \n",
    "  \n",
    "# Fit a SARIMAX(0, 1, 1)x(2, 1, 0, 12) on the training set \n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX \n",
    "  \n",
    "model = SARIMAX(train['#Passengers'],  \n",
    "                order = (0, 1, 1),  \n",
    "                seasonal_order =(2, 1, 0, 12)) \n",
    "  \n",
    "result = model.fit() \n",
    "result.summary() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "bM2gqnbTL6an",
    "outputId": "4f3c9d00-576f-4017-a1c5-fb9994586ede"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f2651a748>"
      ]
     },
     "execution_count": 47,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = len(train) \n",
    "end = len(train) + len(test) - 1\n",
    "  \n",
    "# Predictions for one-year against the test set \n",
    "predictions = result.predict(start, end, \n",
    "                             typ = 'levels').rename(\"Predictions\") \n",
    "  \n",
    "# plot predictions and actual values \n",
    "predictions.plot(legend = True) \n",
    "test['#Passengers'].plot(legend = True) "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Best Time series ARIMA.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}